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Defense vs Offense, How to Win a Championship?

Defense vs Offense, How to Win a Championship?

For years the classic statement in the NFL is that defense wins championships, but offense wins games. I decided to test this theory. I collected data from the past 10 NFL seasons (2011-2021) from every team that finished within the top 5 of defensive or offensive points per game. I then recorded: wins, whether they made the playoffs, distance in the playoffs, and if they made it to or won the Super Bowl. Over the past ten seasons, a top 5 defensive team has won a minimum of 4 and a maximum of 14 regular-season games. The teams within this threshold won on average 10.88 games in the regular season. The average team wins 8 games throughout the regular season, so obviously, this is a large uptick - winning 36% more games than the average team. Out of the 50 eligible teams, 41 made the playoffs, giving the top five defensive teams an 82% playoff rate. The rate an NFL team makes the playoffs over the last 10 seasons was 38.125%, with a minor jump due to the expansion of the playoffs last year. Teams with a top 5 defense were more successful making the playoffs at around a 2.15-1 ratio compared to the rest of the NFL. Of the 41 teams who made the playoffs, 14 lost in the wildcard round, and another 14 lost in the divisional round. That means that 13 out of 41 teams advanced to at least the Conference Championship. The average NFL team has a 12.5% chance of making the Conference Championship while a team with a top 5 defense has a 26% chance. 7 of the 13 teams who made the Conference Championship advanced to make the Super Bowl. In the past 10 seasons, 35% of the Super Bowl appearances were made by a team with a top 5 defense despite only making up 15.6% of the league. Of those 7 teams who made the Super Bowl, 4 won, winning 40% of the last 10 Super Bowls. An NFL team has a 3.125% chance of winning the Super Bowl while a top 5 defense has an 8% chance. This is more than a 250% increase of an average NFL team. A top 5 offensive team has won a minimum of 6 and a maximum of 15 regular season games within the last 10 seasons. The offensive powerhouses have won on average 11.36 games in the regular season. Again, the average NFL team wins 8 games during a regular season, so this is a rather large uptick, winning 42% more regular-season games than the average team. In terms of the postseason, over the last 10 seasons of the 50 teams, 43 of them made the playoffs giving them a 86% playoff rate. As previously mentioned, the playoff rate was 38.125%. Teams with a top 5 offense were significantly more successful in the regular season, making the playoffs at around a 2.25-1 ratio compared to the rest of the NFL. Of the 43 teams who made the playoffs, only 6 lost in the wildcard round, another 13 lost in the divisional round. That means that 24 out of 43 playoff teams made it to at least the Conference Championship. The average NFL team has a 12.5% chance of making the Conference Championship, while a team with a top 5 offense has a 48% chance, nearly 4 times more likely. To further exemplify how successful these offensive teams are, if we were to only use data from playoff teams, those teams would still only have a 33% chance of making it to the Conference Championship. This is about 50% less than a team with a top 5 offense. 13 of the 24 teams who made the Conference Championship went on to make the Super Bowl. In the past 10 seasons, 20 teams have made the Super Bowl. 65% of the Super Bowl appearances have been made by a team with a top 5 offense despite only making up 15.6% of the league. Of those 13 teams who made the Super Bowl, 6 won it, winning 60% of the last 10 Super Bowls. An NFL team has a 3.125% chance of winning the Super Bowl while a top 5 offense has a 12% chance which is a 384% increase of an average NFL team. To even further demonstrate how impressive and successful these teams have been, teams who make playoffs have an 8.2% chance of winning the Super Bowl, which is a 32% decrease from teams who have a top 5 offense despite 7 of teams not even making the playoffs. Both top 5 defenses and offenses have witnessed much more success than the average NFL team over the last 10 years. However, it has become abundantly clear that offense runs the league. The top 5 offensive teams have on average won more regular-season games. The offensive-minded teams also have had a higher playoff rate, outperforming the top 5 defensive teams with 2 additional playoff births. In terms of the playoffs, the 43 offensive teams won on average 1.86 playoff games while also appearing in 13 Super Bowls. Defensively, the 41 teams won on average of 1.24 playoff games and appeared in 7 Super Bowls. Offenses won 50% more playoff games than the defenses and appeared in 86% more Super Bowls. Most importantly, the offense has also won 50% more Super Bowls, outnumbering them 6 to 4. Even more notably, 2 out of 4 of the Super Bowl wins for the defense were from teams who were in the top 5 offensively and defensively. Lastly, the last 5 Super Bowl winners were all in the top 5 in offense, which further proved that the NFL is an offensive league. Sources: Pro-Football-Reference, FootballDB

Statistical Analysis: Ronald Acuña Jr's Dominant Offensive Season

Statistical Analysis: Ronald Acuña Jr's Dominant Offensive Season

Ronald Acuña Jr. has always been a solid player and a fan favorite. This year he has surpassed expectations and has risen the level of his game. He has been a monster offensively carrying his team to many wins. He is establishing himself as a great and solidifying his name amongst the top players of the league. In this article we will examine his offensive play and see how he has grown and compares to the rest of the league This season, Ronald Acuña Jr. is a big contender to taken home the most homers title. His big swings have been extremely rewarding to the Atlanta Braves. He is currently tied for second but has been at the number one spot for several weeks this season. The current standings as of June 6, 2021 are: Acuña Jr.'s dominance of the offensive front has been a pleasure to watch and seeing his current offensive trend, it will be no surprise if he ends up with the most homers in the league at the end of the season. Ronald Acuña Jr. has been a problem since he first step foot into the league, but his rapid rise and growth has been a sight to see. Many baseball fans will appreciate the way he has taken his team on his back and is becoming a franchise player and face for the Atlanta Braves. If we look at his growth from 2018, we see that this season, 2021 year, Acuña Jr. has taken his offensive game to a whole new level. The three statistics we are looking at to document Acuña's progress are BA - batting average, SLG - slugging percentage and OPS - on base percentage plus slugging. BA - Batting average is the most basic batting statistic. It is calculated by taking the number of hits a batter gets and divided it by the number of at bats they take. SLG - Slugging percentage indicated the productivity of a batter. It is the total number of bases the player gets at an at-bat. It is the total number of bases divided by the total number of at-bats a player has. OPS - On base percentage with slugging is an extremely important statistic as it calculates in depth for both the player's ability to get on base as well as the power the player has with each hit. Although the current season is still in progress, Acuña Jr.'s batting average has significantly risen from the past 2020 season. He averaged 0.25 in 2020 and is currently sitting at .249. His slugging average is at an all time high as well. In 2018 he was at 0.552, in 2019 he was at 0.518, 0.581 in 2020 and his current best at 0.611 this season. As we can see through the chart, Ronald Acuña Jr. is having one of his best offensive season and it isn't even over yet! This improvement in numerous statistics show that Acuña has really worked on his game offensively and his offensive dominance is something that will be sustainable as the season continues and future seasons to come. While Acuña Jr. is having his personal best season, how does that compare to the rest of the league? Is he a top player in the league? Let's analyze top 10 players from the most home run's standings. To effectively analyze the top 10 we are taking into account how many runs (R), hits (H), runs batted in (RBI) and total bases (TB) they have. R - Runs is the number of runs a player has scored. H - Hits is the number of hits a player gets. RBI - Runs batted in is a statistic that records how many players/runs a batter has brought in to score because of their hit. TB - Total bases is the accumulation of bases a player has gained through their hits. Looking at these different statistics, the answer to if Ronald Acuña Jr. is a top player in the league is a firm - YES. Acuña Jr. leads the league in runs with 46. His competitors average around 30-40, highlighting the offensive dominance Acuña Jr. has shown this season. Acuña Jr. has batted in 35 runs this season as well, showing the essential role he has to his team. Rounding 116 total bases, Acuña is rising as extremely efficient offensive player. While he still has work to do to be the best and be comfortably at the top, he is growing in the right direction. He fits right in with the best offensive players in the game and with the drastic rise this season, he definitely has the ability and capability to eventually overtake them all. We previously looked at countable, tallied statistics. Now, let's take a deeper look into some percentages. OBP - On base percentage calculates the frequency a batter gets on base. This is calculated by taking the sum of the batter's hits, walks and times hit by pitch and dividing it by the batter's times up to bat. SLG - Slugging percentage indicated the productivity of a batter. It is the total number of bases the player gets at an at-bat. It is the total number of bases divided by the total number of at-bats a player has. OPS - On base percentage with slugging is an extremely important statistic as it calculates in depth for both the player's ability to get on base as well as the power the player has with each hit. WAR - Wins above replacement tells the value of a player. This stat tells how many more wins the current player would have over another replacement player. Ronald Acuña Jr. hold up against the best offensive players in the league. His OBP of 0.386 is third best amongst the top 10 and his SLG of 0.598 and OPS of 0.984 is right there with the top 3. His WAR is at 2.2 which is lower than Guerrero Jr's 3.1, Tatis Jr's 2.9 and Ohtani's 2.6. 2.2 is a respectable WAR value, however, as the season continues and he continues to flourish as an offensive player this is statistic is only going to go up. Looking at his achievements and statistics through a holistic and overall approach, Acuña Jr. is making his case as a future HOF'er and as one of the best in the league. The Atlanta Braves are currently sitting at 2nd in the NL East with a record of 28-29. While considered a good, even great, player before this season, Ronald Acuña Jr has shown his worth this season. His fun game and thrilling home runs make him and his team fun to watch. Through our analysis, we see that he is safely placed in the top positions of several important offensive statistics and is one of the best offensive players this season. However, he is far from done. As the season progresses, Acuña is only going to get better and catch the attention of more people. Cover Art/Design: Fer Basurto References: ESPN, Baseball Reference No copyright infringement is intended The Tactician refrains from monetizing infringing content

Statistical Analysis: Comparing Jack Grealish and "the best of the rest".

Statistical Analysis: Comparing Jack Grealish and "the best of the rest".

Analyzing how Jack Grealish matches up statistically to his English colleagues -- James Maddison, James Ward-Prowse, and Jack Harrison. The Aston Villa man has generated great interest from a host of Premier League clubs and is rumored to become the first 100 million English players, courtesy of Manchester City. Let's see how the new Gascoigne compares to other English midfielders this season. Grealish has been primarily deployed as a left-winger this season due to Ross Barkley occupying the attacking midfield position for Aston Villa this season. Looking at Grealish's heat map we can see that he has stuck to the left flank due to his natural ability to dribble the ball and cut inside to score goals and create chances for his teammates. Considering his abilities Dean Smith sticks to start Jack n the left to create more space as he cuts inside from the left and the right-back is forced to mark him to create a 2v1 situation while attacking. Compared to his three English counterparts, Grealish has 0.41 assists per 90 minutes which is better than the second-best Harrison by an impressive 0.16 assists per/90. He finished the season with 10 assists, number 4 in rank behind Kane (14), Bruno Fernandes (12), KDB (12). Considering that all the mentioned players finished in Europen spots and Aston Villa finished 11th, it certainly speaks about the individual brilliance of Jack this season. While assists can be a skewed statistic with most responsibility riding on the receiver to deliver when presented a scoring opportunity. Expected assists (XA) measures the possibility of a given pass turning into an assist. From the table below we can see that Grealish finished fourth in the league in terms of expected assists, showing his class and ability to play amongst the elite teams and players. Not only has he shown exceptional ability in his passing but also his work rate off the ball making 0.5 interceptions per 90 minutes only second to James Ward-Prowse (keep in mind that Ward-Prowse plays as a center mid) and 1.2 tackles per game. He has a fair share of defensive duties assigned to him and is constantly pressing the right back in order to support his midfield to start a quick counterattack high up on the pitch. England's Finest Analyzing the stats provided by Whoscored we can see that Grealish has played considerably less than his English counterparts but still has managed to top in assists, key passes, and average passes per game. Grealish has attempted 2.5 dribbles per game, being second to Jack Harrison with 1.2 dribbles per game. He has also attracted the most fouls at 4.2 per match. From the visualization below we can see that Grealish has performed unbelievably when it comes to the progressive passes (Prog) which are passes that move the ball towards the opponent's goal at least 10 yards from its further point in the last six passes, or any completed pass into the penalty area. Jack leads the charts with 6.91, with Maddison second at 4.33. Meaning that he outscored everyone in dribbles, key passes, and progressive passes. Furthermore, Jack is also the leader in PPA amongst this group, which points to the fact that Grealish is the clear winner in the midfield when it comes to passing and dribbling. But when it comes to scoring goals he actually comes in last as everyone on the list has scored 8 goals, whilst Jack has only managed to score 6 goals this season. Conclusion Having looked at the four Englishmen's stats from this season and the overall performance analysis of Jack Grealish there is no doubt about his ability in the Premier League this season as one of the finest English players and an all-around midfielder who does not shy away from his defensive duties as well. This is all well noted in the numbers above and is one of the biggest reasons as to why he is headed to the Euros with the England team along with James Ward-Prowse and both Maddison and Harris were left behind. 100 million price tag...That's a lot of money Mate! There are a lot of rumors coming out of Manchester that suggest that Manchester City is ready to make Grealish the first English player worth £100 million. But is that a realistic price tag Citizens are willing to pay? Considering that Harry Kane has received the price tag of £150 million. It is unclear at this stage whether City will splash the cash on the English pair, we will have to wait until the Euro cup is over to see how the transfer market unfolds and whether Grealish will rock the light blue kit of the Premier League champions. Sources: FBref, Whoscored, and Total Football Analysis

Predicting the NBA playoffs using Dean Oliver’s Four Factors of Success

Predicting the NBA playoffs using Dean Oliver’s Four Factors of Success

6/7 Update: Dean Oliver’s four factors of success has been historically great in the first round. With the first round finally being over, the four factors have been almost perfect so far predicting 7/8. With the second round underway in the East, I would watch closely the Bucks vs Nets series as I think the winner of that should come out as the Eastern conference champions. James Harden's injury should have a huge effect in the series, and in the grand scheme of the playoffs. For the Bucks 3pt shooting will be a huge factor as in game 1 loss they shot 20% from distance. In the Hawks vs 76ers series with one game under the belt, the key elements will be Joel Embiid’s health as they are two different teams with and without him and the Hawks shooting. Along with those factors the 76ers will need to limit Atlanta’s high pick and roll as Trae Young this whole playoffs has been on a tear scoring and passing wise. In the West on the other hand we have our predicted champions the Utah Jazz well rested going into the second round. They will be facing the Clippers who are coming out of a hectic 7 game series. The key in this matchup will most likely be the consistency of Paul George and the shooting of the Jazz. Both teams are great defensively as well. We have our first incorrect prediction with Suns knocking off the Lakers. As I mentioned in the article a big factor for the Lakers success would be their health as it was ultimately their downfall. I need to note that this takes nothing away from what the Suns accomplished in this series and playoffs so far as they did win the one game where both teams were “healthy”. Denver Nuggets after a battle with the Blazers are now set to play the Suns. The key factors in that series will be CP3’s health and the play of Michael Porter Jr who has averaged 21.3 ppg in the playoff wins, while only averaging 14 ppg along with 0 total assists in the 2 losses. The 2021 NBA Playoffs are underway with the first round starting on Saturday. What is on everyone’s mind is who is will be the next NBA champion and which teams will under or over-perform. In this article, we will predict this year’s playoffs using Dean Oliver’s Four Factors of Success and measure its accuracy to predict the outcomes of the last 5 NBA Playoffs excluding the bubble year (2020). Data was collected for each team that made the Playoffs since 2015 (2015-2021) excluding last year due to COVID-19 and opt-outs going into the bubble. As a parameter, if two teams are within .15 points of each other, it is deemed a toss-up. When a matchup is a toss-up, the win would be awarded to the team with the higher seed. Each playoff bracket is predicted with no reseeding as that is how the NBA playoffs work. The Four Factors that Dean Oliver uses to determine success are a unique combination of effective field goal percentage, free throw rate, offensive rebound rate, and turnover percentage (all apply both offensively and defensively). In this year’s NBA playoffs, Dean Oliver's Four Factors of Success predict that the Utah Jazz will be the 2021 NBA champions, defeating the Milwaukee Bucks in the Finals. The Jazz is number 1 in both the offensive and defensive four factors, while the Bucks are in the top 5 in both. Some of the more interesting picks while using the four factors would be the Hawks going to the Eastern Conference Finals after narrowly beating out the 76ers, and the Suns as the 2nd seed, losing in the first round. It is important to mention that a few of the teams ended up with bad draws in terms of who they are playing as both the Clippers and the Nets were in the top 4 of the Four Factors however, they both had bad draws in the second round. Another point to note is that teams like the Lakers and Nets were not fully healthy throughout the regular season, with both teams playing less than 10 games with their active starting lineups. Even with those injuries the Nets still managed to rank 4th in the overall Four Factors ranking, while the Lakers were not so far behind in 7th. Both teams may potentially outperform their expectations due to those reasons and the model also had them both just narrowly losing in their respective matchups. The previous 5 NBA champions from 2015-2019 had an average Four Factor score of 1.84 with three of the champions being within .1 of the average. This year there are 3 teams in the playoffs that have a higher score than that: the Jazz, Clippers and the Bucks. The Nets slightly trail behind. The lowest score of the previous 5 champions was the 2019 Toronto Raptors with a score of .955. This year, the teams that are above that score are the ones previously listed as well as the Nuggets and the Hawks. Slightly behind the Raptors, would be the Lakers and the 76ers who have both dealt with some injury issues with their stars during the regular season which may have caused their score to drop a little. After collecting data of playoff teams from the last 5 regular seasons and using the Dean Oliver Four Factor predictions for each respective year, we were able to determine how accurate this model has been. Over 2015-2019 the model has correctly predicted 57/75 playoff series giving it a success rate of 76%. When predicting the first round, the model was correct 35/40 series giving it a success rate of over 87% with the worst year only getting 2 matchups wrong. The model had a little more difficulty when it came to predicting the second round as it was correct for 13/20 series which would give it a 65% success rate. When it comes to the Conference Championship, the model correctly predicted 7/10 series winners for a 70% success rate in the conference championship which is impressive. For the championship, the model correctly chose the champion 2/5 times, and if it were not for blown 3-1 leads it would be 3/5. Lastly, 4/5 champions over the past 5 seasons were in the top 5 of the Four Factors ratings. Dean Oliver's Four Factors of success definitely is a strong predictor in the playoffs, especially in the earlier rounds. It was also interesting to note that all besides one of the champions were within the top 5 of the four factors in their respective years. References: https://www.nba.com/stats/teams/four-factors, https://www.basketball-reference.com/playoffs Illustration by Fernanda Basurto

How do the freshman seasons of the last ten No. 1 Picks in the WNBA Draft compare to Paige Bueckers?

How do the freshman seasons of the last ten No. 1 Picks in the WNBA Draft compare to Paige Bueckers?

The last ten No. 1 Picks in the WNBA draft have had, and continue to have, incredible basketball careers. Nneka Ogwumike (2012 No. 1 Pick) averaged 10.6 points with a 65.7% true shooting percentage in her freshman year at Stanford. In 2016, she became the most efficient shooter in the history of the WNBA with a true shooting percentage of 73.7% while averaging 19.7 points. She is also the president of the WNBPA. Brittney Griner (2013) set the WNBA season record for blocks in her rookie season with 129. She has also led the WNBA in scoring twice: averaging 21.9 points in 2017 and 20.7 in 2019. In August 2020, Chiney Ogwumike (2014) became the first WNBA player and the first black woman to host a national radio show for ESPN. She continues to play in the WNBA. Jewell Loyd (2015) won the USBWA National Freshman of the Year award over Breanna Stewart and since then, has matched Stewart in WNBA Championship titles. Breanna Stewart (2016) has won almost every award under the sun, including 4 NCAA Championships with UConn and 2 WNBA Championships with Seattle. She also set the WNBA record for most defensive rebounds in a season with 277 in her first WNBA season. Kelsey Plum (2017) scored 57 points on her senior day to become the NCAA All-Time Career Scoring Leader with 3,527 points (25.4 points per game). She is also the NCAA Single Season Scoring Leader with 1,109 points (31.7 points per game). On Martin Luther King Jr. day this year, a statue of A’ja Wilson (2018) was unveiled on South Carolina’s campus to remember her impact on the program, including leading South Carolina to their first Final Four appearance in 2015 and first National Championship in 2017. Jackie Young (2019) played an integral role in Notre Dame’s 2nd NCAA Championship in 2018, especially in their win over the undefeated UConn team in the Final Four. Against UConn, Jackie had 32 points, a career high and the 7th highest ever scored in the Final Four. Sabrina Ionescu (2020) holds the NCAA Career Record (men or women) for Triple Doubles with 26, which is 14 more than second place. She was also the first player in history (men or women) to reach 2,000 points, 1,000 assists, and 1,000 rebounds. Charli Collier (2021), this year’s #1 pick, rocketed to the top with large improvements in each of her 3 seasons, averaging 13.1 more points per game in her junior year than in her freshman year. Together, these 10 women have earned the AP Player of the Year 8 times, the Wooden Award 9 times, the Naismith Trophy 8 times, the USBWA Women’s National Player of the Year 8 times, and the WNBA MVP title 3 times. They hold 7 NCAA Championship Titles and 6 WNBA Titles. But, there’s four things that none of them were able to do: earn the Wooden Award, Naismith Trophy, AP Player of the Year, or USBWA Player of the Year as a freshman. Paige Bueckers just finished her freshman season by winning all four of these awards. And it wasn’t just the last ten No. 1 WNBA picks that weren’t able to do it. Bueckers is the first woman in the history of college basketball to receive these awards as a freshman. Needless to say, her debut season has garnered a lot of attention; here’s what Diana Taurasi had to say. To see what set her freshman season apart, the following article compares Paige Bueckers’ freshman season to the freshman seasons of the last ten No. 1 Picks in the WNBA Draft. Paige Bueckers averaged 20.03 points per game, second only to Kelsey Plum’s 20.94 points per game. Despite playing their freshman seasons 7 years apart, these two are connected: Adia Barnes was an assistant coach at the University of Washington during Plum’s freshman season, and Barnes was the head coach of the University of Arizona program that put an end to Bueckers’ historic freshman campaign. Bueckers averaged 36.14 minutes per game, second only to Plum’s 37.29 minutes per game. The player with the best points to minutes ratio was A’ja Wilson, who averaged 13.14 points per game in only 19.84 minutes. The following visualization ranks all 11 players by various shooting statistics. Guards are denoted in blue, forwards/centers are in green, and Bueckers (a guard) is in red. Bueckers is the clear leader in field goals made per game, with 7.66, and has the highest field goal percentage of all of the guards (52.36%). While she averaged less 3 point shots per game than Plum or Sabrina Ionescu, she has the highest 3 point shooting percentage at 46.38%. Bueckers is a lot more than a shooter. She has more assists per game (5.79) than anyone, including Ionescu, the queen of Triple Doubles. She surpassed the UConn freshman single season assist record in her 20th game of the season (she played 29 games in total) and set the UConn single game assist record with 14 against Butler. When it comes to turnovers, Bueckers managed to average less than guards Plum and Ionescu, with Jewell Loyd being the only guard averaging more than 30 minutes per game with fewer turnovers than Bueckers. Bueckers blows everyone away in the steals department with 2.28 per game, the only player averaging over 1.5 steals per game. She is also the most accurate free throw shooter, hitting 86.9% of free throws. Finally, it is not pictured on the visualization, but Bueckers averaged more blocks than any other guard and had more defensive rebounds than every guard besides Ionescu. The last part of evaluating a freshman season is consistency, especially in the NCAA Tournament: do they rise to the occasion or falter? Kelsey Plum (the highest scorer) is the only player on this list of 11 whose team did not make it to the NCAA Tournament. Interestingly enough, 6 of the 11 women were eliminated in the Final Four in their freshman season. Also, 4 of the 11 women were eliminated by UConn, and two more played for UConn. The following graph tracks each player’s scoring progression throughout the season. The players are ordered by average points, with Plum having the most and Jackie Young having the least. Right away, we can see that Stewart is the epitome of playing your best when it matters the most; her NCAA Tournament run is impressive. Bueckers had three 30+ games in a row in the middle of the season. She dropped down to 11 points 4 games later, but steadily built back up through the rest of the season. She demonstrated incredible poise. Nneka Ogwumike stepped up in the NCAA Tournament, especially with 27 points against San Diego State in the Second Round. Jewell Loyd also played better in the NCAA Tournament, including 27 points in the First Round. Brittney Griner’s scoring slowly decreased after her 34 point game against Oral Roberts early on in the season, and Charli Collier also performed worse in the NCAA Tournament. Paige Bueckers displayed phenomenal control throughout her freshman season. A component of her season was that 6 of her 11 teammates were also freshmen, and there were not any seniors on the roster. Maybe that forced her to be more of a leader, maybe it didn’t. In any case, she was firing on all cylinders in more than one aspect on the court. Look at the following graph. It puts 8 important statistics to the game on the same scale in order to compare them. Bueckers accompanies the most room on the graph. If Paige Bueckers can have a career that is anything like the other 10 players mentioned in this article, it will go down in history. Regardless of what her future holds, we know it will be fun to watch. No copyright infringement is intended The Tactician refrains from monetizing infringing content

Analyst Insights: Steven Scott

Analyst Insights: Steven Scott

The Tactician speaks to Steven Scott, former manager of football services at FootballLOGIQ and currently an on-field performance and player recruitment analyst at the Canadian Premier League. We discuss Steven's experience as an analyst in Canada and his formation of a private consultancy focused on sustainable club growth. How long have you been in the sports industry and what positions have you held? I have been a coach for 8 or 9 years and technically we are doing a lot of analysis there, I have also worked as an analyst for TSN ( A Canadian broadcasting station). For over 2 years now I have been working as a data analyst, completely immersed in the data analytics side of the sport. What is a typical word day like as a performance analyst? My work is divided into two aspects, the first is identifying key KPIs (Key Performance Indicators) for individual clubs. For example, if a certain club wants to play a high pressing game, I translate that playing style with KPI's that are most relevant. In the high press example, I would present statistics like possession gained within 6 seconds of losing the ball. The second part of my job involves speaking with agents and analyzing players from a data perspective and also from a video perspective. A lot of work revolves around identifying markets around the world and analyzing different metrics for player recruitment purposes. What analytical tools do you use as a performance analyst? Most of my work is on spreadsheets based on the data pulled from Wyscout and Opta. Sometimes I use Python and Tableau. But Tableau can be a bit tricky to work with as it is a good visualization tool, but at the same time limits the users in the design interface. What I like about Tableau is that it can translate the data very quickly. Is there a certain project or report you worked on which influenced a club to acquire/scout a player? In the Canadian Premier League, we work with the 21st Club by utilizing their data and football development which I am a part of. We have a vetting process for international players based on their playing level and wages, several players that have signed with the CPL have been analyzed by me and others in the department. The main purpose of the league is to make us a soccer nation and make our national team better. What is the hardest part of the job? The hardest part is to identify the blind spots present in the data and understanding the power it has and also the flaws in the data. Knowing how the data is collected and what doe these data points to capture. That is the most difficult thing in the industry when it comes to analysis. For example, defining a pass is easy for the regular viewer but as an analyst looking at a simple pass means discovering the intent behind the pass and why certain if not all actions are made during the game. The ability to find the right player for the right club at the right time is incredibly difficult but we do our best. What do y0u look for in a player during data scouting? From a non-data point of view, some players I scout are technically more gifted than others but then during the data analysis it comes down to is that player just technically gifted or is efficient as well? Can he make the killer passes into the box versus the counterparts, it goes to show that technique vs efficiency has a big part to play in scouting. For example, if we are looking at a center back that is not technically gifted but has a counterpart that is left-footed, quicker, and technically gifted we need to figure out what the team needs are and how a player can influence the style of play. Can you share about your consultancy venture and what do you aim to achieve? link: https://www.stevenmarcscott.com/ I started my consultancy for a couple of reasons, mainly due to help out clubs in the given situation of the pandemic. Some clubs in the Bundesliga went bust because of no ticket revenue and a lot of the wages being inflated. My main objective is to figure what an adequate wage bill should be for each player based on the performance in the past year or two. Furthermore, use the same models to identify transfer fees so the clubs are making smarter decisions and grow sustainably. The next step for football clubs is to identify alternative revenue streams in case of another situation like COVID. In the past couple of years is there a player in Canada or the United States that has caught your eye? It is quite difficult to find another player like Fonzie (Alphonso Davies) or Jonathan David for a bit but it is completely possible. In CPL Marco Bustos has impressed me this year and received a national call-up this season. Mo Farsi (right-back for Cavalry FC) has been phenomenally good right back, I could see him making a move away from Canada sometime in the future. In terms of raw talent, Alphonso is probably the best Canadian athlete of the decade. Any word of advice you wish to share with aspiring football analysts. As a piece of advice, the most important thing is domain knowledge, starting with when and how to receive the ball in a certain situation. The basic tactical side of things will go a long way, you can learn tools online for free but domain knowledge is something we should focus on. At SportLOGIQ I had to create a data collection system from the ground up, it was challenging for sure but it made me learn about how and which data points are most important and relations that matter.

Analyzing the best defenders at playing out from the back

Analyzing the best defenders at playing out from the back

Statistical analysis of Premier League defenders who are best at playing out from the back by utilizing K-Means clustering analysis on possession and passing statistics. Furthermore, computing scores by creating a ranking-based scoring metric for passing and possession play. Football is constantly going through a transformation in tactical approaches and ever since the arrival of Pep Guardiola the culture of attacking football has transitioned to tiki-taka, rondo, and attractive approach of possession play. The major drivers in this new era are passing from advanced areas or progressing the ball into wider areas in the pitch. The most important part of this possession play aka playing out from the back is the involvement of defenders in the attacking plays. The center backs and goalkeepers are now considered the first attackers of the ball and playmakers at the same time. The figure provided by Opta sports portrays a clear picture of the passing domination of the big-6 compared to the EPL average. In 2012/13 the average number of passes attempted by center backs was 30 per 90 minutes. Fast forward to 2018/19 where average passes per 90 minutes have increased 50% to 60 passes per 90 minutes. The trend not only applies to the big-6 but the rest of the EPL has also followed the Guardiola philosophy of "Possession is the best form of defense". Dashboard The given dashboard is a scouting tool that compared defenders from the premier league based on 6 statistics. Live Passes (live ball touches), total touches, 1/3 possession (carries that enter the final third of the pitch), progressive distance, and the possession score. Possession score is calculated by: possession Score = live touches + successful dribbles + carries+ 1/3 possession + carries into the 18 yard box - no. of dispossessed - Mis (failed to control the ball) + number of targetted passes. Click here to interact with the dashboard on full screen. Key Takeaways: As predicted Man City defenders are leading in all given statistics, the likes of Kyle Walker, Jaoa Cancelo, and John Stones are leading in total touches, progressive passes, and possession score. Ruben Dias (509.7), John Stones (506), and Thiago Silva ( 493) are the top 3 leaders in the possession scores. Consistent with my assumptions that Man City defenders are a class apart in terms of possession. Surprisingly John Stones leads the league in Progressive distance, beating every fullback and his Man City colleagues. Cluster Analysis While the dashboard is a greater tool for comparing stats, we will dig further to understand the skills needed to excel at playing out at the back. Therefore, to account for the best passers of the ball, I use a scoring model based on 8 variables to rank defenders. The ranks are based on the given model: Passing scores: Passes completed (Long, medium, and short) + total distance of the passes + progressive distance of the passes + KP (passes that directly lead to a goal + 1/3 (passes that enter the final third). The interactive figure below illustrates three different clusters based on the passing scores on the y axis and possession scores on the x-axis. The following analysis uses a machine learning model known as K-Means clustering to group similar players together based on their respective scores. The three clusters are ranked from red being the best, blue, and yellow being the mid-tier, and lower tier of the clusters respectively. Click here to view the chart in full screen. Key Takeaways: The surprise Inclusion of Luke Ayling at the top of the red cluster points to his work rate to excel in Marco Beisla's impressive Leeds team. Making 34 appearances this season covering an average of 18 yards per game. As expected the yellow cluster consists of mostly mid-table and lower position teams, as these teams are more focused on long balls rather than possession play, eg: New Castle, Burnley, and Crystal Palace. Three Arsenal players David Luiz, Gabriel, and Rob Holding make it to the red cluster providing a good example of how Mikel Arteta's side relies heavily on possession play and not achieving optimal results. Playing out from the back has provided mixed results in the EPL. While clubs like Man City are a great example of how possession play and high-intensity pressing provide a better quality of football. Clubs like Arsenal and Liverpool point out the flaws in playing out from the back and high press done wrong can be quite disastrous. Many clubs if not most expect certain clubs to make mistakes while playing through the back lines and choose to stay back and let them make mistakes. The table below shows how Liverpool has given away a staggering 11 goals this season due to errors. While playing out from the back worked for Liverpool in the first season, this season has proved to be disastrous without the likes of Van Dijk and Matip for a majority of the season. While the system under Klopp remains the same the quality of football has dropped due to relying on players on Phillips and Kabalk to replicate VVD. Hence this season is a great example of the pros and cons of playing out from the back and maybe the quality of players is just as important as the system, formation, and tactics. References data: fbref.com https://www.statsperform.com/resource/passing-out-at-the-back/

Four-seam, Changeup, Slider, Curve or Cutter? A Statistical Breakdown of Pitch Usage

Four-seam, Changeup, Slider, Curve or Cutter? A Statistical Breakdown of Pitch Usage

What makes a pitcher an elite pitcher in the MLB? Is it the mastery of one pitch or the reliance on a repertoire of possible pitches they can throw at a batter and throwing the right one at the right time? This article will analyze and breakdown the pitches and scenarios from the best 5 pitchers of the 2019 season to find the answer to the question - how many types of pitches does a pitcher need to be successful in the MLB? To analyze what type of pitches the best pitchers throw, we will look at the top 5 pitchers with the most strikeouts from the last full MLB season, 2019. We will look at the percentage of times they throw a four-seam, changeup, slider, curve and for some pitchers, cutter, against left and right handed batters. The breakdown of pitches is divided into different in-game situations. We have when the pitcher is ahead of the count, when the count is 2-2 (even), when the pitcher throws the first pitch, for any count situation, for when the pitcher has two strikes and lastly when the batter is ahead in the count. 1. Gerrit Cole - 326 Strikeouts The first pitcher we will look at is Gerrit Cole. He was the top pitcher in 2019 in regards to strikeouts, recording a league high of 326 strikeouts. Gerrit Cole heavily relies on his four-seam in any scenario. It can be seen that against left handed hitters, in all pitching situations, Cole goes to the four-seam the majority of the time. Some notable percentages are that he throws the four-seam in all counts 56% of the time and 65% of the time when the pitcher is ahead. This shows that this is Cole's most reliable and favorite pitch. When the pitcher is ahead of the count, the pitcher wants to throw his best to gain back traction and if Cole throws the four-seam 65% of the time in that scenario, he definitely believes it is his strongest pitch. He also throws his slider and curve against left handed hitters in the range of 10-20 percent. While he relies of the four-seam, he effectively throws the slider and curve as well, with the top percentage in the scenarios being: 21% throwing a slider when he has two strikes and 24% of a first pitch curve. Against right handed hitters, Cole again goes to the four-seam the majority of the time. When the batter is ahead, Cole goes to the four-seam 59% of the time. He uses the four-seam at a very high percentage and the only pitch that comes close to his usage of a four-seam is his slider. He uses the slider a convincingly 36% of the time when he is ahead in the count, 37% when he has two strikes on him and about 30% of the time in other scenarios. There is a lot of strategy in Cole's pitching, but there's no denying that he favors the four-seam. With this analysis, we can come to the conclusion that Gerrit Cole is the type of pitcher who relies primarily on the four-seam but mixes in a curve and slider against left handed hitters and a slider against right handed batters. 2. Justin Verlander - 300 Strikeouts Next, we will look at Justin Verlander. He had an incredible 300 total strikeouts during the 2019 season. With the breakdown above, we can see that like Cole, Verlander loves to throw the four-seam in any scenario. Looking at his breakdown against left handed hitters, we can see that when the batter is ahead in the count, Verlander goes to the four-seam pitch 67% of the time. For 22% in that scenario, Verlander throws a slider. In a any count situation, 52% of the time Verlander throws a four-seam, 22% of the time he throws a slider, 19% of the time he throws a curve and 6% of the time he throws a changeup. These statistics show us that while Verlander does throw a variety of pitches, he is most comfortable with his four-seam and slider. Against right handed hitters, Verlander does not favor the four-seam as much as he did against left handed hitters. In situations where the pitcher is ahead in the count and when he has two strikes, Verlander prefers to throw his slider. With the slider percentage being 47% and 49% for those scenarios and the four-seam percentage being 31% for both those scenarios. While the four-seam is a big part of his game, and statistically seems to be his favorite pitch, he knows different scenarios requires different pitches. So, in contrast to Cole who primarily relies on four-seam for any batter he faces, we can see that Verlander takes a different approach when it comes to his pitching. Verlander heavily uses his four-seam but it's not the only pitch that he uses in high volume. With this analysis, we can come to the conclusion that Justin Verlander is the type of pitcher who primarily uses the four-seam against left handed hitters and interchanges between the four-seam and slider for right handed batters, while also using the curve at a respectable percentage against both batters. 3. Shane Bieber - 259 Strikeouts Third on the list is Shane Bieber. Shane Bieber had an outstanding 2019 season, recording 259 total strikeouts. Shane Bieber uses both his four-seam and curve at a high percentage against left handed hitter. In the all counts scenario, Bieber uses his four-seam 46% of the time and his curve 31% of the time. When the count is even, the percentages are 48% and 31%. When the pitcher is ahead in the count, Bieber uses the four-seam 40% and the curve 41%. Lastly, when Bieber has two strikes, he goes to the four-seam 43% of the time and the curve 38% of the time. The percentages are very similar which show that his comfort and usage level of both pitches are high. He can choose between the two pitches and use which pitch he thinks will trick the batter more rather than just honing in on the four-seam like some other pitchers. Against right handed hitters, Bieber again uses the four-seam but also equally uses the slider. The four-seam and slider percentages in every scenario is extremely close and similar. The maximum percentage differential between the two usages are 5 percent. The chart above shows, that for right handed hitters, all the other scenarios have almost identical pitch usage percentages between the four-seam and slider pitch. This highlights Bieber as a very skilled pitcher. The batter cannot expect which pitch will be thrown at them in different scenarios because Bieber throws a variety of pitches in different scenarios, making him a very difficult pitcher to read and to hit against. Shane Bieber is a very balanced pitcher. While he does use the four-seam a lot, the usage percentage of that pitch does not heavily outweigh any other pitch. With this analysis, we can come to the conclusion that Shane Bieber is the type of pitcher who uses the four-seam, curve and slider all interchangeably and frequently. 4. Jacob deGrom - 255 Strikeouts Next we have Jacob deGrom. A phenomenal pitcher, deGrom thrived in the 2019 season and threw a respectable 255 strikeouts. Jacob deGrom is a prime example of a pitcher who relies on a variety of pitches. Against left handed batters he uses the four-seam, slider and the changeup. The four-seam usage ranges in the 40%, the slider usage ranged in the 25% and the changeup ranges in the 25-30%. deGrom uses the four-seam most on the first pitch, When throwing the slider, it is most probable he will throw it in a two strikes scenario. He would most likely use a changeup when the batter is ahead. We can see that deGrom does not rely on just one pitch. He prefers to use different pitches in scenarios in order to play the mental baseball game. He keeps the pitcher guessing and uses the unknown factor as an advantage in his pitching. Against right handed hitters, deGrom again uses the slider and the four-seam. In the all counts scenario, deGrom uses his four-seam 50% of the time and his slider 38% of the time. On the first pitch, deGrom uses his four-seam 59% of the time and his slider 32% of the time. In batter ahead situations, the four-seam is used 49% of the time and the slider is used 39% of the time. When the count is even the percentages are 54% and 34%. When the pitcher is ahead in the count, deGrom uses the four-seam 46% and the slider 42%. Lastly, when deGrom has two strikes, he goes to the four-seam 43% of the time and the curve 42% of the time. Since deGrom uses an array of pitches, he has the challenge to work extremely hard to make sure every pitch he uses is as effective as the next. deGrom uses three different pitches when pitching! He has a lot of baseball IQ when pitching and loves to keep the batters he faces on their toes. With this analysis, we can come to the conclusion that Jacob deGrom is the type of pitcher who like Bieber, throws a variety of pitches. He is the type of pitcher who uses the four-seam, changeup and slider all interchangeably and frequently. 5. Trevor Bauer - 253 Strikeouts The last analysis will be on Trevor Bauer. Fifth on the list of total strikeouts in the 2019 season, Bauer had 253 total strikeouts and set his name on the list of top elite MLB pitchers. Trevor Bauer relies on primarily two pitches against left handed batters: the four-seam and the curve ball. To highlight some percentages, Bauer throws the four-seam 58% of the time when the batter is ahead and 48% of the time on the first pitch. When the pitcher is ahead in the count, Bauer throws his curve 57% of the time and when the count is even he throws curve 24% of the time. This shows that this is Bauer is reliable on both his four-seam and curve ball. Bauer has spent time developing quality pitches and uses his mastery of multiple pitches as part of his strategy to outperform the batters and get the strikeouts and outs. Against right handed hitters, Bauer again uses the four-seam but changes up his strategy and uses the cutter more than the curve ball. He at times also uses the slider. His four-seam takes the majority of the pitch usage but he uses the cutter 20% of the time in all counts, 31% of the time for the first pitch, 31% of the time when the batter is ahead, 24% of the time when the count is even, and 7% when the pitcher is ahead and when there are two strikes. He uses the slider 23% of the time in all counts, 15% of the time for the first pitch, 10% of the time when the batter is ahead, 21% of the time when the count is even, 27% when the pitcher is ahead and 36% when there are two strikes. Trevor Bauer fits into the jack of all trades idea - where it's better be good at multiple pitches and decide what pitch to throw based on the scenario. With this analysis, we can come to the conclusion that Trevor Bauer is the type of pitcher who analyzes different scenarios and responds with either a fourseam and curveball against left handed batters and with either a four-seam or cutter against right handed batters. Coming back to the question, how many pitches do you need to be a successful pitches in the MLB, we can see that elite pitchers have at least two pitches that they rely on. Some extremely elite pitchers like Gerrit Cole can do with using one the most of the time, but it seems that the rest of the pitches have more of a range they use and use each pitch based on the batter they face and the count scenario they are currently in. As pitchers continue to envole in the league and new pitches enter, it will be interesting to see if the trend of rotating between two-three good, developed pitches will continue or if pitchers will add more to their repertoire or fall into a more single minded, one pitch mastery approach. References: Baseball Reference, Brooks Baseball No copyright infringement is intended The Tactician refrains from monetizing infringing content

Which Team Has Been the Most Inconsistent in the Last Decade of March Madness?

Which Team Has Been the Most Inconsistent in the Last Decade of March Madness?

In March Madness, sometimes a team that is expected to win it all is eliminated in the Round of 64, and sometimes a team that is expected to be eliminated in the Round of 64 remains standing long after the first round of the tournament. Surprises like this happen every year, but is there one team that stands out for their inconsistency? The following article determines the team that has been the most inconsistent in the last 10 Men’s NCAA Tournaments by comparing the differences in a team’s elimination round year-to-year. For the last 10 March Madness tournaments, each team was categorized by which round of the tournament, or step of the tournament, they were eliminated in. For each year of the last 10 NCAA tournaments that a team made, the absolute change in their elimination round year-to-year was calculated. Then the average of these changes were found. The years where a team did not make the NCAA tournament were not included so as to not give teams that only made the tournament a few times a false sense of consistency because they had many years where they made it to the same step: not qualifying. This allows us to compare teams that qualified all of the last 10 years to teams that only qualified 6 of the last 10 years. Here’s an example of how we quantified a team’s variability: Texas Tech has made it to the NCAA Tournament four times in the last 10 years. From 2016 to 2018, they saw an absolute change of 3 steps. From 2018 to 2019, they saw an absolute change of 2 steps. From 2019 to 2021, they saw an absolute change of 4 steps. This averages to 3 steps. Therefore, Texas Tech’s year-to-year variability in the NCAA Tournament is 3 steps. This calculation was performed for every team that had more than one NCAA Tournament appearance in the last 10 years. As a note, 82 teams have only had one appearance in the NCAA Tournament in the last 10 years. Only 14 of these teams made it at least one step further than expected (click here to see these teams). South Carolina made it 3 steps further than expected in 2017, from the Round of 32 to the Final Four. Here is a series of charts of year-to-year variability by team, where each page represents teams with a different number of March Madness appearances over the last 10 years. Use the arrows to navigate between pages. Teams that were eliminated at the same step of the tournament in every appearance (for a total variability of 0) were not included in these graphs. There are only 4 teams that have made it to all 10 of the last March Madness tournaments: Michigan State, North Carolina, Kansas, and Gonzaga. We gave Gonzaga a hard time this year. We claimed their regular season schedule wasn’t difficult enough to prepare them for the tournament. But they are one of only four teams to play in the last 10 tournaments, and of those four teams, Gonzaga is tied with Kansas for the least year-to-year variability. Of teams with 9 appearances, Villanova has the most variability. They won the NCAA Tournament in 2016 and 2018, but in 2017 they were eliminated in the Round of 32 despite being expected to win. They were also eliminated in the Round of 32 in the year before their 2016 National Championship title and in the year after their 2018 National Championship title. Of teams with 8 appearances, Virginia has the most variability. In 2018, they were expected to win it all, but were eliminated in a shocking upset in the Round of 64. They came back in 2019 to become National Champions, only to be eliminated in the Round of 64 again in 2021. Now all of the teams were combined into one chart, no longer separated by number of appearances, to determine which team has been the most inconsistent in the NCAA tournament. Connecticut has far and away the most variability of any team in the last 10 March Madness tournaments. Here’s what their 5 appearances have looked liked (with their predicted elimination round included): They were very hot and cold, either being eliminated in the first two rounds or winning it all. However, they have matched or bettered their seed in every appearance except 2021. Connecticut did not have the pressure of being expected to win, or even make it to the Final Four or Elite 8. Here is a graph of UConn's offensive and defensive ratings over the last 10 years (not including 2020). The two years they won it all are marked with that year’s Final Four logo and their other 3 March Madness appearances are denoted with the March Madness logo. Their play during the season definitely correlates with how far they made it into the NCAA Tournament with one exception. Their offense in 2021 puts them in line with their 2011 Championship team and above their 2014 Championship team, and their defense is not far behind their 2011 or 2014 teams, but they were eliminated in the first round of the tournament. Plus, they were only expected to get to the second round of the tournament. Playing the game at the same level as they did in 2011 and 2014 isn’t enough in 2021. Data from https://www.kaggle.com/andrewsundberg/college-basketball-dataset and https://barttorvik.com/trank.php# Cover photos from Hartford Courant and UConn Today No copyright infringement is intended The Tactician refrains from monetizing infringing content

How Well Did We Predict the Last Decade of March Madness?

How Well Did We Predict the Last Decade of March Madness?

The 2021 Men’s NCAA Tournament ended with Baylor dominating a heavily favored Gonzaga team in the Championship game. In fact, there were many surprises throughout the tournament, including UCLA making it all of the way to the Final Four from the First Four (the play-in games) and Illinois (predicted Runner Up) falling to Loyola Chicago in the Round of 32. How do the surprises in the 2021 Men’s NCAA Tournament compare to previous years? The following article evaluates how well the basketball world was able to predict the outcomes of the last 10 Men’s NCAA Tournaments. Data was compiled for each team that competed in the last 10 March Madness tournaments (2011-2021 because the 2020 March Madness tournament was canceled due to the COVID-19 pandemic). Each team was categorized by which round of the tournament, or step of the tournament, they were predicted to be eliminated in. A team's regional seed was used to determine their predicted elimination round (and the AP Poll was used to differentiate the predicted Champion and Runner Up from the other two regional No. 1 seeds). Teams that were supposed to be eliminated in the First Four (the play-in games) were counted as step 1. Next, the difference between when a team was predicted to be eliminated and the step of the tournament where they were eliminated was calculated. The chart above is an example of the two teams predicted to be eliminated in the Final Four. Team A gained two steps from their predicted elimination round, becoming the Champions. Team B lost two steps from their predicted elimination round, losing in the Sweet 16. Therefore, they combined for a total change of 4 steps from their predicted elimination rounds. For the teams in each predicted elimination round of the last 10 NCAA Tournaments, the total change from seed to outcome was calculated. Then, each elimination round was put on the same scale because the number of teams differ in each group. For each elimination round, the year(s) with the smallest total change were assigned a value of 0 and the year(s) with the largest total change were assigned a value of 10. This shows how the distribution of variability by predicted eliminated round changed year-to-year. For example, in 2011, teams expected to be eliminated in the Elite 8 only varied a little from this prediction, but in 2012, the teams expected to be eliminated in the Elite 8 ended up being eliminated in very different stages of the tournament from the Elite 8 (in comparison to the other years). In 2012, the top seed accounted for a smaller portion of total variability than it did in the other years because it reached the step it was predicted to reach: the top seed won the tournament. This is the only year in the last 10 NCAA Tournaments that the top seed came out victorious. It was Anthony Davis’ Kentucky Wildcats. The year with the most variability for the top seed was 2018 when the Virginia Cavaliers lost to No. 16 UMBC in the Round of 64, a loss of 6 steps from their seed. The following two charts (use the arrows to navigate between them) illustrate the variability between years in different ways. Rather than using percentages, these graphs break down the variability in each year by using the total steps gained or lost (absolute value) by all of the teams in each predicted elimination round. In the first chart, we can see how each predicted elimination round contributed to the total variability and compare total variability across the years. We can see that 2017 had the least total variability from predictions. 2021 is tied with 2018 for most overall variability in the last 10 March Madness tournaments. It makes sense that the outcomes of the 2021 March Madness Tournament were harder than average to predict. We didn’t have how teams played in the 2020 March Madness Tournament to factor into our predictions, and there was a lot of COVID-19 chaos in the regular season and the tournament. In the second chart, we can more clearly see the change in variability for each predicted elimination round over the years. 2015 had the least variability for teams predicted to lose in the Round of 64. 2013 had the least variability in the Round of 32, 2017 for the Sweet 16, 2019 for the Elite 8, and 2016 for the Final Four. In 2016 and 2017, the predicted Runner Up was the Runner Up. From the first graph, we can see that the total variability from year to year is random; we have not become better at predicting the outcomes of March Madness in the last decade. From the second graph, we can see that the average variability from the two teams predicted to be eliminated in the Final Four is almost the same as the average variability of the team predicted to win it all (3.2 steps for Final Four vs 3.1 steps for Top Seed). The two teams predicted to lose in the Final Four are combining to gain or lose 3.2 steps from their predicted elimination round, which the top seed is single handedly matching. This speaks to the pressures facing the top seed of the tournament. Even though 15 of the 20 teams predicted to make the Final Four in the last decade did not make it that far, they were still eliminated closer to their predicted final round of the tournament than the top teams were. The pressure facing the top team can force them to crash and burn early in the tournament. Data from https://www.kaggle.com/andrewsundberg/college-basketball-dataset and https://barttorvik.com/trank.php# Cover photos from The Dallas Morning News and UConn Today No copyright infringement is intended The Tactician refrains from monetizing infringing content

Analyzing Home Advantage: How Fans Change the Game

Analyzing Home Advantage: How Fans Change the Game

The 2021 MLB season has started with a bang with several teams having successful home openers. Since COVID, the game of baseball has definitely changed, with the most noticeable change being empty stadiums. In this article, we will examine those empty stadiums and see as fans start re-entering the field how beneficial/impactful the home field advantage really is. Summary During the 2020 season with no fan attendance, there was a minimal differential between games won at home or away. During the past 2019 season, we see that teams like the Houston Astros, Colorado Rockies and Texas Rangers have done significantly better at home than away games. There also seems to be a common trend amongst the league with the home victory ratio slightly edging over the away ones. As fans come back to stadiums in 2021, we may see teams grabbing a few more victories at home than the past seasons. Teams love playing at home and they celebrate the home opener. But is the love of home games and the fanfare surrounding it perpetuated by fan excitement or do teams actually play better at home? When playing at home, teams not only have the fan and field familiarity advantage, they also bat at the bottom of each inning and thus have the last at-bat which can be very beneficial especially in close and tie games. As a whole, teams generally perform slightly better at home. In 2018, teams won 52.5% of all home games and in 2019, the home team won 52.9% of all games played. Are these numbers small statistical coincidences or do they actually mean that teams have a small advantage to win the game when playing at home? Let's take a look! With the unique COVID 2020 season where there were no fans in attendance, now is the perfect time to examine each team and see if home advantages really make an impact on the outcome of games and to explore the differences between home and away games. 2020 Season - Control Group First, we will look at each team's home wins and losses as well as away wins and losses in the 2020 season through a radar chart. This season was played with COVID regulations* so we can so it serves as the basis for how teams would perform without fans. The 2020 season acts as the controlled variable. Covid Regulations* The season will be played without fans in attendance. If the game goes into extra innings, the game will continue with the International ruling which allows the teams to have a runner placed on second base. Institution of three-batter minimum rule that requires pitchers to face at least three pitchers before being taken out. The season will be shortened to 60 games. The radar chart above is used to express the four variables: home wins, home losses, away wins and away losses so we can compare them to see which is most prominent. We can see how the cluster stretches and which variable holds the most weight. In this article, we will mainly be focusing on how the home wins compare to the other variables and how many games teams are winning at home. It is important to note that all teams play the same amount of home and away games during the regular season so we can make direct comparisons. For this season, we will assume that a 10 game differential is significant between home and away wins. Through the radar chart, we can see that some teams play exceptionally at home. The New York Yankees played won 22 out of 31 games while winning only 11 out of 29 on the road. The Minnesota Twins won 24 games and only lost 7 at home compared to their away record of 12 wins and 17 losses. The Houston Astros won 20 games at home and only won 9 on the road. The Tampa Bay Rays won 20 games both at home and away while losing only 9 at home compared to the 18 when playing away. Another team with significantly more wins at home than away are the Philadelphia Phillies. A team that faltered at home and capitalized on away games are the Miami Marlins. The rest of the teams have an incredibly close home and away win ratio with the results depending on the respective team's caliber rather than the fact if they were playing at home or away. Next, we will take a closer look at the home wins compared to the away wins. This season has a shortened schedule, so every team did not play the same amount of home and away games as in previous seasons. To get an accurate visual representation, the home and away wins data has been normalized by taking the number of wins and dividing it by the total number of games played (at home or away). This will allow us to get a better understanding of how the home and away wins compare to each other and whether the fan atmosphere makes a difference to the professional teams. Examining the win ratios, this chart shows us that some teams have performed overwhelmingly better at home than on the road. These teams are the Texas Rangers, Toronto Blue Jays, Pittsburgh Pirates, and the Arizona Diamondbacks. On the other side of the stick, some teams have done relatively worse at home than away. These teams are the Colorado Rockies, Miami Marlins, and the Boston Red Sox. These teams however are the extreme cases and can be seen as the exception because the rest of the 23 teams have very similar ratios and statistically the outcome of their game isn't impacted by whether they are playing at home or away. 2019 Season - Experimental Group Next, we will look at each team's home wins and losses as well as away wins and losses in the 2019 season through a radar chart. This season was the latest season completed before any regulations were put into so it serves as the data for how teams would play games when the home team had the "home team advantage" of fans. The 2019 season acts as an experimental group where we can see how teams perform with the extra edge. When looking at the clusters we can see that some teams truly use their fan advantage and are dominant at home! The Houston Astros is one of them. The Astros cluster is shifted towards the home wins category showing that they have won 60 games at home compared to only winning 47 on the road. The Colorado Rockies have won 43 games at home while only winning 28 of the total away games and The Texas Rangers won 12 more games at home - winning 45 at home and 33 on the road. Other teams that have a ten-game or more win differential at home than on the road are the Los Angeles Dodgers, Chicago Cubs, and the New York Mets. 6 out of the 30 teams have a distinct stronger home record than away record. Home wins are not as common or as significant as one would assume with all the hype and fanfare that surrounds it. The Minnesota Twins have won 9 additional games on the road, Boston Red Sox have won 8 and the Baltimore Orioles have won 3. Many teams perform very similarly on the road and at home. The Tampa Bay Rays won 48 games both home and away and lost 33 games at home and away and as a result, its cluster is a perfect diamond - showing the dame distribution of home and away wins and home and away losses. The rest of the teams have very similar wins and losses both at home and away with most of them around a 3-6 game differential. Next, we will focus in on the home wins compared to the away wins to get a more detailed visual understanding of how the two categories compare. This chart confirms the previous conclusions that the majority of teams perform the same at home or on the road but the detailed separation of wins based on home or away also shows that there is a pattern of the home wins just being slightly greater than away wins for majority teams. Some teams like the Houston Astros have a strong grasp of their field and have taken full advantage of playing at home while others like the Texas Rangers struggle more at home. But, through the analysis of the radar chart and looking closely at the home and away wins we can see that home wins slightly edge over the away wins. The slighter greater home wins for the majority of the teams imply that there may be a slight benefit to teams playing at home. Many would discredit this slight advantage and say it's just a coincidence but since it's continuously true for numerous of the MLB teams it's something that cannot be ignored. It seems that the atmosphere fans create, adds some magic and comfort that players feel when playing home that could be contributing to this slightly greater chance to win when teams play at home. 2021 Season The 2021 season started on April 1. The analysis of the previous two seasons, show that fans can have a slight impact on the game. As COVID vaccines are being distributed the teams have allowed limited fan seating based on their state regulations. Below are a list of teams with the total fans allowed to see the games as well as the percentage of stadium allowed to be filled converted to a decimal. With fans back to cheering for their teams, we can look at the teams that have currently played 3 or more games and see how they are playing at home. With the small pool of data we have since the season just become, we can see that same trend as in 2019 where there seems to be a better chance for teams to win and home and the home games end up having more wins than losses. As the season progresses and the stadiums continue to open up and more and more fans enter the stadium, it is very possible that teams may end up gathering a few extra wins at home. While the advantage is not significant and game-changing, there is something special about playing at home and the fans and players know it! References: ESPN, Baseball Savant No copyright infringement is intended The Tactician refrains from monetizing infringing content

Analyzing the Rapid Rise of Adia Barnes' Arizona Wildcats

Analyzing the Rapid Rise of Adia Barnes' Arizona Wildcats

After beating UConn in the Final Four, Arizona’s NCAA Tournament run came to an end in a heartbreaking one-point loss to Stanford in the Championship game. However, it was still a historic season for the Wildcats. Adia Barnes brought the Arizona women to their first Final Four and National Championship appearances after only 5 years as the head coach of Arizona. She brought this program to new heights at a dizzying speed. The following article analyzes the improvements Arizona has made over the last 5 years and explains how Adia Barnes made it all possible. Adia Barnes has been elevating her team off the court as well: they have recorded a team GPA that is the highest in program history and they lead the Arizona Athletic department in community service hours for multiple years. Here is a breakdown of Barnes' 5 years at Arizona: The graph above plots the scoring margin of all of Adia Barnes’ games coaching Arizona against the date. If the scoring margin is above 0, it means the Wildcats won that game, and vice versa. Wins or Losses can be highlighted using the menu on the right, as well as their history against specific teams. Check out their history against Stanford. In the 2016-17 and 2017-18 seasons, the vast majority of Arizona's games were losses. In 2018-19, they won the WNIT title, a tournament for teams that don’t make it to the NCAA Tournament. In 2019-20, they were gearing up to compete in the NCAA Tournament before the COVID-19 pandemic hit. In 2020-21, they made it all of the way to the Championship Game in the NCAA Tournament. Let’s say that again: Adia Barnes brought her team from a 20% win percentage in 2017-18 to a 78% win percentage and an NCAA Championship Game appearance just three seasons later. How did she do it? You can’t talk about Arizona without talking about Aari McDonald. She played in the 2016-17 season for the University of Washington, where she was the 3rd leading scorer as a freshman. She then transferred to Arizona and had to sit out the 2017-18 season due to transfer rules. She returned to the game in 2018-19 with a vengeance and hasn’t looked back since. For all four of Aari's statistics in the graph below, there is a large jump from the 2016-17 season to the 2018-19 season. In the case of turnovers, the jump is in the wrong direction because Aari was much more involved in the game. For 3 pointers made and turnovers, Aari continued to improve after 2018-19 in the 2019-20 and 2020-21 seasons. However, the year with her highest average points and free throw attempts (while shooting free throws at about the same percentage in her last 3 seasons) was 2018-19. Arizona still saw significant improvements between 2018-19 and 2020-21, but Aari’s game stayed at the same level in some areas. They weren’t even one of the 64 teams invited to the tournament in 2019. Fast forward to 2021, and they were not only invited to the tournament, but they were one of the last two teams standing. Aari has been a steady presence for Arizona since 2018-19. But what has continued to fuel their rise to the top? To start, Barnes is a great recruiter. Current Junior Forward Cate Reese was Arizona’s highest-ranked recruit ever and Arizona’s first McDonald’s All-American ever. Keep in mind she was a senior in high school during Arizona’s season with a 20% win percentage. Cate didn't commit to Arizona because of what she saw on the court on game day, but Barnes managed to convince her to give Arizona a shot. And the good thing she did: Cate’s averaged 12.3 points per game over her 3 seasons with Arizona. Guard Shaina Pellington was a redshirt Junior this season following her transfer from Oklahoma after 2 seasons. She scored 15 points against Stanford in the Championship game, second only to Aari’s 22 points. Barnes is also great at developing talent once they get to Arizona. Senior Forward Sam Thomas has been with Arizona since its 6-24 2017-18 season. It’s only fitting that she scored 12 points in Arizona’s Final Four victory over UConn, second only to Aari’s 26 points. Barnes has done a great job of developing a scrappy team that plays very well together. Just look at the graph below breaking down steals per game by season. The average number of steals Arizona has made each season has been increasing since 2017-18, despite facing tougher competition as they rise through the ranks. Defensive rebounds have been increasing, fouls have been decreasing, and turnovers have been decreasing per season. See graphs of these statistics here. With all of that being said, Aari McDonald stepped up in the NCAA Tournament. The following series of graphs (click on the arrows to go between graphs) shows Aari’s points, free throw attempts, and 3 pointers made per game in the 2020-21 season. Games played in the NCAA Tournament are distinguished with their opponent’s team logo. Particularly in the UConn and Stanford games, Aari made herself a huge target, hence a large number of free throws attempted, while still managing to hit more 3’s than average. Watch out for Aari McDonald. She has a bright future ahead of her in the WNBA. And watch out for Arizona. With Adia Barnes at the helm, you’ll be hearing a lot about Arizona Women’s Basketball for years to come. Video Credits: ESPN No copyright infringement is intended The Tactician refrains from monetizing infringing content