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FCC — Sports Blackouts (updated October 1, 2014)

October 7, 2014 Comments off

Sports Blackouts
Source: Federal Communications Commission

The Federal Communications Commission repealed its sports blackout rules, which prohibited cable and satellite operators from airing any sports event that was blacked out on a local broadcast station. This action removes Commission protection of the private blackout policies of sports leagues, which require local broadcast stations to black out a game if a team does not sell a certain percentage of tickets by a certain time prior to the game. Elimination of this rule, however, may not end all sports blackouts: sports leagues may choose to continue their private blackout policies through contractual arrangements with programming distributors. For more information read the news release.

A “sports blackout” occurs when a sports event that was scheduled to be televised is not aired in a particular media market. A blackout may prevent transmission of sports programming on local broadcast networks and/or non-broadcast platforms such as cable and satellite television.

Hat tip: PW

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CRS — Questions Raised About NFL’s Tax-Exempt Status, Legal Sidebar (September 17, 2014)

September 25, 2014 Comments off

Questions Raised About NFL’s Tax-Exempt Status, Legal Sidebar (PDF)
Source: Congressional Research Service (via Federation of American Scientists)

With all the attention the National Football League (NFL) has received regarding its handling of several high-profile controversies, questions have arisen about the League’s tax status. The NFL is exempt from federal income taxes as an organization described in § 501(c)(6) of the Internal Revenue Code. (Note this applies only to the League—the teams are not tax-exempt).

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.

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.

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