Archive for the ‘professional sports’ Category

A Data-driven Method for In-game Decision Making in MLB

August 11, 2014 Comments off

A Data-driven Method for In-game Decision Making in MLB (PDF)
Source: MIT Sloan Sports Analytics Conference

In this paper we show how machine learning can be applied to generate a model that could lead to better on-field decisions by predicting a pitcher’s performance in the next inning. Specifically we show how to use regularized linear regression to learn pitcher-specific predictive models that can be used to estimate whether a starting pitcher will surrender a run if allowed to start the next inning.

For each season we trained on the first 80% of the games, and tested on the rest. The results suggest that using our model would frequently lead to different decisions late in games than those made by major league managers. There is no way to evaluate would have happened when a manager lifted a pitcher that our model would have allowed to continue. From the 5th inning on in close games, for those games in which a manager left a pitcher in that our model would have removed, the pitcher ended up surrendering at least one run in that inning 60% (compared to 43% overall) of the time.

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The Three Dimensions of Rebounding

August 8, 2014 Comments off

The Three Dimensions of Rebounding (PDF)
Source: MIT Sloan Sports Analytics Conference

The recent spread of tracking technology in sports is bringing about a new era in analytics where we can deconstruct things we previously understood as one thing. We consider rebounding in basketball. Until recently we would get at most one piece of information after a missed shot: the name of a player that got the rebound. In this paper, we (1) describe the full timeline of a rebound, (2) develop metrics for the various dimensions of this timeline using novel techniques and (3) apply them to calculate individual player abilities in these dimensions.

The Hot Hand: A New Approach to an Old “Fallacy”

August 7, 2014 Comments off

The Hot Hand: A New Approach to an Old “Fallacy” (PDF)
Source: MIT Sloan Sports Analytics Conference

The vast literature on the Hot Hand Fallacy in basketball rests on the assumption that shot selection is independent of player-perceived hot or coldness. In this paper, we challenge this assumption using a novel dataset of over 83,000 shots from the 2012-2013 National Basketball Association (NBA) season, combined with optical tracking data of both the players and the ball. We create a comprehensive model of shot difficulty using relevant initial shot conditions, and use it to show that players who have exceeded their expectation over recent shots shoot from significantly further away, face tighter defense, are more likely to take their team’s next shot, and take more difficult shots. We then turn to the Hot Hand itself and show that players who are outperforming will continue to do so, conditional on the difficulty of their present shot. Our estimates of the Hot Hand effect range from 1.2 to 2.4 percentage points in increased likelihood of making a shot.

Can’t Buy Much Love: Why money is not baseball’s most valuable currency

August 6, 2014 Comments off

Can’t Buy Much Love: Why money is not baseball’s most valuable currency (PDF)
Source: MIT Sloan Sports Analytics Conference

Talking heads, disgruntled general managers, and bar-goers have argued for years that professional baseball has a major competitive balance problem stemming from payroll inequities. This paper examines, and eventually rejects, that notion, relying on both a cross-sectional and longitudinal empirical analysis. Despite rising payroll inequality, the percent of variation in wins that can be explained by payroll (referred to throughout the paper as the “Win Buying Index”) has been declining relative to historical values and the NFL and NBA . This drop in the Win Buying Index can be attributed to a dramatic rise in the production of young , pre-free agency eligible players with severely suppressed salaries . As the pro-ready age for young athletes continues to fall and leagues continue to enact stricter regulations against substances that had historically disproportionately helped older players, policies that ensure cheap young labor — the league’s most valuable currency — will do more to reduce the effect s of payroll on winning than restrictions on overall team salary.

What Does it Take to Call a Strike? Three Biases in Umpire Decision Making

August 5, 2014 Comments off

What Does it Take to Call a Strike? Three Biases in Umpire Decision Making (PDF)
Source: MIT Sloan Sports Analytics Conference

Do Major League Baseball umpires call balls and strikes solely in response to pitch location? We analyze all regular season calls from 2009 to 2011 — over one million pitches — using non-parametric and structural estimation methods. We find that the strike zone contracts in 2-strike counts and expands in 3-ball counts, and that umpires are reluctant to call two strikes in a row. Effect sizes can be dramatic: in 2-strike counts the probability of a called strike drops by as much as 19 percentage points in the corners of the strike zone. We structurally estimate each umpire’s aversions to miscalling balls and his aversions to miscalling strikes in different game states. If an umpire is unbiased, he would only need to be 50% sure that a pitch is a strike in order to call a strike half the time. In fact, the average umpire needs to be 64% sure of a strike in order to call strike three half the time. Moreover, the least bias ed umpire still needs to be 55% sure of a strike in order to call strike three half the time. In other words, e very umpire is biased. Contrary to their formal role as unbiased arbiters of balls and strikes, umpires are biased by the state of the at-bat when deciding whether a pitch intersects the strike zone.

“Win at Home and Draw Away”: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors

July 28, 2014 Comments off

“Win at Home and Draw Away”: Automatic Formation Analysis Highlighting the Differences in Home and Away Team Behaviors (PDF)
Source: MIT Sloan Sports Analytics Conference

In terms of analyzing soccer matches, two of the most important factors to consider are: 1) the formation the team played (e.g., 4 – 4 – 2, 4 – 2 – 3 – 1 , 3 – 5 – 2 etc. ), and 2) the manner in which they executed it (e.g., conservative – sitting deep, or aggressive – pressing high). Despite the existence of ball and player tracking data, no current methods exist which can automatically detect and visualize formations. Using an entire sea son of Prozone data which consists of ball and player tracking information from a recent top – tier prof essional league, we showcase an automatic formation detection method by investigating the “home advantage”. In a paper we published recently, using an ent ire season of ball tracking data we showed that home teams had significantly more possession in the forward – third which correlated with more shots and goals while the shooting and passing proficiencies were the same. Using our automatic formation analysis, we extend t his analysis and show that while teams tend to play the same formation at home as they do away, the manner in which they execute the formation is significantly different. Specifically, we show that the position of the formation of teams at home is significantly higher up the field compared to when they play away. This conservative approach at away games suggests that coaches aim to win their home games and draw their away games. Additionally, we also show that our method can visually summarize a game which gives an indication of dominance and tactics. While enabling new discoveries of team behavior which can enhance analysis, it is also worth mentioning that our automatic formation detection method is the first to be developed.

FIFA — Laws of the Game 2014/2015

July 17, 2014 Comments off

Laws of the Game 2014/2015 (PDF)
Source: FIFA

Authorised by the International Football Association Board This booklet may not be reproduced or translated in whole or in part in any manner without the permission of FIFA. Published by Fédération Internationale de Football Association, FIFA-Strasse 20, 8044 Zurich, Switzerland


Subject to the agreement of the member association concerned and provided the principles of these Laws are maintained, the Laws may be modified in their application for matches for players of under 16 years of age, for women footballers, for veteran footballers (over 35 years of age) and for players with disabilities. Any or all of the following modifi cations are permissible:
• size of the field of play
• size, weight and material of the ball
• width between the goalposts and height of the crossbar from the ground
• duration of the periods of play
• substitutions

Further modifications are only allowed with the consent of the International Football Association Board.

Categories: FIFA, professional sports

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