Updated: Feb 17, 2012, 5:15 PM

Analyze This: The MIT Sloan Analytics Papers


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And you thought Selection Sunday was intense. In the past few months, the organizers of the annual MIT Sloan Sports Analytics Conference have sifted through more than 100 submissions to determine the 10 finalists for its Research Paper of the Year. Now we need your help. Read the summaries of each paper below, then scroll down to cast your vote or leave a comment. The fans' choice will be announced at this year's conference, taking place March 2 and 3. To activate your neurons, we offer teasers of each paper below. Go ahead -- get your geek on.


Deconstructing the Rebound with Optical Tracking Data
By Rajiv Maheswaran, Yu-Han Chang, Aaron Henehan and Samantha Danesis

ESPN Simple Summary
Rebounding isn't the sexiest stat, but doing it efficiently leads to wins. This quartet studied the impact that such factors as shot location and rebound height have on offensive rebounding rates. Memo to Kevin Love: Your secret might be out.

Authors' Abstract
This paper leverages STATS' SportsVu Optical Tracking data to deconstruct several previously hidden aspects of rebounding. We are able to move beyond the outcome of who got the rebound to discover the non-linear relationship between shot location and its impact on offensive rebound rates, implications of the height of where rebounds are obtained, and estimates of where players should move in order to improve rebounding rates. We also leverage machine-learning methods to estimate the predictability of rebounding.

Number to Know
15

Percentage of rebounds that hit the floor before being collected.

Read full paper online


The Effect of Fan Passion and Official League Sponsorship on Brand Metrics
By Kirk Wakefield and Anne Rivers

ESPN Simple Summary
Tom Brady sips his Smart Water on TV. Somewhere, his sponsors at Glaceau smile. See. Try. Like. Do. It's a marketing equation that's stood the test of time. Wakefield and Rivers examined how fan passion translates into sponsor value in the NFL.

Authors' Abstract
We examine the role of official NFL sponsorships in five primary categories to determine the relative effects of an official sponsorship on each element of the BAV brand equity model, Differentiation, Relevance, Esteem, and Knowledge among NFL fans versus non-fans over the 2008-2010 time period. Importantly, we compare the effects against primary competitors within each category targeting the same NFL fan audience. Results show the benefits of sponsorships over and above the brands' national campaigns. Category specific results show the importance of longitudinal participation and analysis including competitors and fans of the property versus non-fans otherwise exposed to the brand's marketing strategy.

Number to Know
16,000

Number of people polled in the three years this study was conducted.

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Big 2's and Big 3's: Analyzing How a Team's Best Players Complement Each Other
By Robert Ayer

ESPN Simple Summary
By looking at various two- and three-player combinations in the NBA, the study identifies the most and least effective types of star lineups.

Author's Abstract
One of the most important aspects of team construction is identifying and acquiring the most talented and productive players on your team, the players on whom a team's fortunes most rely. Teams must decide which player-types, when combined, yield the best fit. As an example, suppose there is a team, whose current best player is a scoring, shoot-first point guard. Suppose this team is looking to bring in a top-flight free agent. What type of player should this team target? Should they bring in a defense-oriented big man? Should they acquire a multi-faceted, jack-of-all-trades wing? This paper aims to answer these questions. Analyzing player data and team season data from 1977, this paper first uses clustering techniques to group players into appropriate groups, then regression to determine the degree to which the composition of a team's top 2 and top 3 players affect that team's win total, while accounting for team quality and coaching ability. This paper shows that the composition of a team's top 2 and top 3 players is a strongly statistically significant factor in the success of a team, and shows which combinations yield over-performance, and which combinations yield underperformance, relative to the team's talent and coaching quality.

Number to Know
23.19

The coaching coefficient (a measure of coaching impact) for the Spurs' Gregg Popovich, tops among head men in the study.

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Predicting the Next Pitch
By Gartheeban Ganeshapillai and John Guttag

ESPN Simple Summary
Every hitter is a threat to go yard when he knows what's coming. This paper seeks to improve pitch-prediction ability by using real-time info -- pitch count, for example -- along with more advanced stats, such as prior pitcher/batter battles.

Authors' Abstract
If a batter can correctly anticipate the next pitch type, he is in a better position to attack it. That is why batteries worry about having signs stolen or becoming too predictable in their pitch selection. In this paper, we present a machine-learning based predictor of the next pitch type. This predictor incorporates information that is available to a batter such as the count, the current game state, the pitcher's tendency to throw a particular type of pitch, etc. We use a linear support vector machine with soft-margin to build a separate predictor for each pitcher, and use the weights of the linear classifier to interpret the importance of each feature. We evaluated our method using the STATS Inc. pitch dataset, which contains a record of each pitch thrown in both the regular and post seasons. Our classifiers predict the next pitch more accurately than a na´ve classifier that always predicts the pitch most commonly thrown by that pitcher. When our classifiers were trained on data from 2008 and tested on data from 2009, they provided a mean improvement on predicting fastballs of 12.5% and a maximum improvement of 50%. The most useful features in predicting the next pitch were Pitcher/Batter prior, Pitcher/Count prior, the previous pitch, and the score of the game.

Number to Know
12.5

Percentage of improvement in pitch-prediction accuracy offered by this study over previous models.

Read full paper online


An Expected Goals Model for Evaluating NHL Teams and Players
By Brian Macdonald

ESPN Simple Summary
Fenwick and Corsi ratings go only so far in revealing NHL player value. This analysis digs deeper, looking at four seasons of faceoffs, hits, turnovers and zone starts, among other factors, to determine expected goal output.

Author's Abstract
One difficulty with analyzing performance in hockey is the relatively low scoring rates compared to sports like basketball. Fenwick rating (shots plus missed shots) and Corsi rating (shots, missed shots, blocked shots) have been used to analyze players and teams because they have been shown to be better than goals as a predictor of future goals. In this paper, we use variables like faceoffs, hits, and other statistics as predictor variables in addition to goals, shots, missed shots, and blocked shots, to predict goals. Our models outperform previous models with regard to mean squared error of actual goals and predicted goals. The results can be interpreted as expected goals and can be used in adjusted plus-minus models instead of goals. We use ridge regression to estimate a player's contribution to his team's expected goals per 60 minutes, independent of his teammates, opponents, and the zone in which his shifts begin. We also give adjusted plus-minus estimates based on goals, shots, Fenwick rating, and Corsi rating and use these results alongside the results for expected goals to provide an additional means by which NHL analysts, decision- makers, and fans can measure how valuable a player is to his team.

Number to Know
3.2

Goals per 60 minutes that the Bruins score when Nathan Horton is on the ice, compared with 2.43 goals per 60 minutes when he's off it.

Read full paper online


CourtVision: New Visual and Spatial Analytics for the NBA
By Kirk Goldsberry

ESPN Simple Summary
Which NBA players truly have the deepest range? Goldsberry reveals the answer to that and other questions regarding players' shooting ability by using visual and spatial analytics to divide the court into 1,284 shooting "cells." Steve Nash and Ray Allen can claim their crown now.

Author's Abstract
This paper investigates spatial and visual analytics as means to enhance basketball expertise. We introduce CourtVision, a new ensemble of analytical techniques designed to quantify, visualize, and communicate spatial aspects of NBA performance with unprecedented precision and clarity. We propose a new way to quantify the shooting range of NBA players and present original methods that measure, chart, and reveal differences in NBA players' shooting abilities. We conduct a case study, which applies these methods to 1) inspect spatially aware shot site performances for every player in the NBA, and 2) to determine which players exhibit the most potent spatial shooting behaviors. We present evidence that Steve Nash and Ray Allen have the best shooting range in the NBA. We conclude by suggesting that visual and spatial analysis represent vital new methodologies for NBA analysts.

Number to Know
1,071

Number of cells that Kobe Bryant has shot from during the past six seasons.

Read full paper online


Using Cumulative Win Probabilities to Predict NCAA Basketball Performance
By Mark Bashuk

ESPN Simple Summary
Bashuk introduces a metric called WPI -- win probability index -- that measures the impact that each play in an NCAA men's basketball game has on a team's chances of winning. WPI can be used to reveal which players had the biggest hand in a game's outcome.

Author's Abstract
Traditional ranking methods such as Average Scoring Margin (ASM) or the Ratings Percentage Index (RPI) are limited in their accuracy and usefulness because they focus on just the final score of the game, not how the game arrived at the final score. In this paper I propose a new method that looks at cumulative win probabilities over the duration of a game to measure both team and an individual player's performance. Using five years of game-play data to generate a Win Probability Index for NCAA basketball, I am able to create an open system that allows anyone to measure the impact, in terms of win probability added, of each play. This method is more accurate than either the ASM or RPI while also providing a more detailed level of player and play specific detail. My initial design includes input adjustments for conference play, home/away/neutral site games, and a team's strength of schedule. Outputs of this study include player rankings, team rankings, and strength of schedule rankings. Detailed explanations of my methodology and the simulations used to build the model, a comparison to existing methods, and an exploration of futures uses of this data are included.

Number to Know
4.74

Points that home-court advantage is worth in NCAA basketball.

Read full paper online


NBA Chemistry: Positive and Negative Synergies in Basketball
By Allan Maymin, Philip Maymin and Eugene Shen

ESPN Simple Summary
Who says chemistry can't be quantified? This paper's trio used each NBA player's offensive and defensive skills to come up with optimal lineups and mutually beneficial trades.

Authors' Abstract
We introduce a novel Skills Plus Minus ("SPM") framework to measure on-court chemistry in basketball. First, we evaluate each player's offense and defense in the SPM framework based on three basic categories of skills: scoring, rebounding, and ball-handling. We then simulate games using the skill ratings of the ten players on the court. The results of the simulations measure the effectiveness of individual players as well as the 5-player lineup, so we can then calculate the synergies of each NBA team by comparing their 5-player lineup's effectiveness to the "sum-of-the-parts." We find that these synergies can be large and meaningful. Because skills have different synergies with other skills, our framework predicts that a player's value is dependent on the other nine players on the court. Therefore, the desirability of a free agent depends on the players currently on the roster. Indeed, our framework is able to generate mutually beneficial trades between teams. Other ratings systems cannot generate ex-ante mutually beneficial trades since one player is always rated above another. We find more than two hundred mutually beneficial trades between NBA teams, situations where the skills of the traded players fit better on their trading partner's team.

Number to Know
15.1

Points per game by which a team composed of LeBron James and four replacement-level players would outscore a team of five replacement-level players.

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Experience and Winning in the National Basketball Association
By James Tarlow

ESPN Simple Summary
This analysis looks at whether experience really matters in the quest for NBA postseason success. Turns out, while prior postseason experience increases the odds that a team makes the playoffs, it doesn't help it win when it gets there.

Author's Abstract
When commenting on the ability of NBA teams it is commonplace to cite a young team's inexperience as a negative and the experience of a veteran laden team as a positive. However, there is a lack of empirical investigation into the effects of player or coach experience on team performance. In this paper I analyze the effects of player, coach, and team experience levels on franchise postseason wins. This study uses hand gathered panel data detailing the 804 NBA seasons played by 30 NBA franchises between 1979 and 2008. I find that increased postseason player experience increases a team's ability to make the playoffs while not increasing their ability to win in the playoffs. A coach's postseason experience does contribute to a team's ability to win in the playoffs. I also find that teammate experience, a proxy variable for team chemistry, significantly increases a team's postseason success. I also offer plausible explanations for these effects. These results should be of interest to team executives, league analysts, and NBA commentators as it provides quantitative insight to an issue that has previously been based almost entirely on conjecture.

Number to Know
1.31

Estimated number of postseason wins attributable to every 4.33 years of an NBA player's regular-season experience.

Read full paper online


Effort vs. Concentration: The Asymmetric Impact of Pressure on NBA Performance
By Matt Goldman and Justin M. Rao

ESPN Simple Summary
This survey shows that at the end of a close game, a player shooting a free throw is affected much differently by pressure than a player attempting to grab an offensive rebound.

Authors' Abstract
How and why does performance change under pressure? Psychologists have argued that pressure can both distract, motivate and generate too much self-focus (thinking about the details of how one should accomplish a goal, as opposed to "just doing it"). Studies have implicated self-focus as the key factor in pressure-associated performance declines. To understand if these results extend to highly trained experts, we examine two fundamentally different actions within the context of the same professional sport, basketball. The first action, free throw shooting, requires quiet concentration, while the second, offensive rebounding, is based on effort exerted in the heat of the moment. Home vs. Away variation allows us to understand how a supportive audience moderates the impact of pressure. We find that home free throw shooters do significantly worse in clutch situations, with the effect being larger for poor shooters. Road players show no change in behavior under pressure, indicating distraction plays a limited role in this task. In stark contrast, the home team gets significantly better at offensive rebounding in pressure packed moments, while again the road team shows no relationship between performance and pressure. The results show a clear asymmetric impact of a supportive audienceit can both inspire effort and lead to detrimental self-focus, even for experienced agents. From a sports perspective, it shows how the traditional notion of home-court advantage is not inconsistent with some pressure-related disadvantages ("home choke").

Number to Know
0.5

Improvement, in percentage points, of home players' free throw shooting in nonclutch situations compared with clutch situations.

Read full paper online

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