Statistical Confirmations of Steroid Use? - PowerPoint PPT Presentation

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Statistical Confirmations of Steroid Use?

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Title: Statistical Confirmations of Steroid Use?


1
Statistical Confirmations ofSteroid Use?
  • Andy Dolphin
  • Raytheon Company
  • August 6, 2008

2
Outline
  • Background
  • Adjusting Statistics
  • Quantifying Players Abilities
  • The Mitchell Report Sample
  • Culling the Statistical Records

3
Background
  • Astronomy
  • Stellar populations in nearby galaxies
  • Data analysis techniques
  • Sports
  • Analysis and prediction of team performances
  • Baseball player projections and analysis
  • Coauthor of The Book Playing the Percentages in
    Baseball
  • Consultant for Cleveland Indians

4
Adjusting Player Stats
  • We need a way to determine if a players
    performance has improved or degraded.
  • Critical aspect
  • We dont care if a players performance is better
    than other years
  • We do care if his performance is better than
    would be expected.

5
Adjusting Player Stats
  • Factors affecting a players performance
  • Age
  • Home ballpark
  • Strength of league
  • Usage (relief vs. starting pitchers)
  • Teammates (players do not face them)
  • For consistency, player statistics adjusted to
    age 25 and to the NL strength of their rookie
    seasons.

6
Raw vs. Adjusted Stats
7
Year-to-Year Correlations
  • By comparing adjusted metrics over many seasons,
    one can determine how much players deviate from
    average career trajectory.
  • For both hitters and pitchers, multiplying number
    of PAs by 0.9?year gives fairly constant
    prediction accuracy.

8
Raw vs. Adjusted Stats
9
Characterizing Player Ability
  • Need a metric that includes entire effect on
    games outcome.
  • For example, OBP considers a walk and home run as
    equals.
  • Solution each outcome is scored based on its
    average effect on the teams winning probability,
    relative to an out.
  • A single is worth about 0.07 wins.
  • This metric tends to be about 1/10 of batting
    average.

10
Characterizing Player Ability
  • Need a metric that is indicative of players
    ability.
  • For example, a pitchers win-loss record heavily
    depends on run support and fielding.
  • Solution adjust outcome rates to reflect the
    degree in which they are indicative of a players
    abilities (regression towards mean).
  • For example, a hitter retains about 40 of his
    single-hitting rate from year to year, compared
    with under 20 for a pitcher.

11
Player Career Trajectories
  • Selected players listed in the Mitchell Report

12
Career Trajectory Roger Clemens
13
Career Trajectory Andy Pettitte
14
Career Trajectory Barry Bonds
15
Career Trajectory Rafael Palmeiro
16
Mitchell Report Sample
  • The Mitchell Report identified players suspected
    of steroid use, as well as specific years in
    which purchases could be tracked.
  • Do players show better performance in these
    seasons, compared with their career baseline?
  • Do players listed show more deviation than
    average over their careers?

17
Mitchell Report Single Seasons
  • 32 hitters played 63 seasons with 300 PA
  • Average improvement 3.4 1.2
  • The only statistically-significant sample came
    from the BALCO-tied players, who averaged about a
    10 increase in production.
  • 16 pitchers played 35 seasons with 300 PA
  • Average improvement 3.3 1.5

18
Mitchell Report Single Seasons
  • Problems with this analysis
  • Mitchell Report specifically identifies years in
    which players purchased drugs from particular
    sources, not the entire time of use.
  • Significant performance swings can be masked by
    the statistical uncertainties with even a full
    season of data.
  • There is likely a correlation between injuries
    and steroid usage that needs to be accounted for.

19
Mitchell Report Careers
  • Do players listed in Mitchell Report have larger
    than average variation from typical career
    trajectory?
  • Hitters variation/avg 1.10 0.08
  • Again, only statistically significant sample
    comes from BALCO players
  • Pitchers variation/avg 1.09 0.12

20
Spotting Unusual Players from the Statistical
Record
  • Instead of looking at specific players for signs
    of improvement, what if we look for players based
    on unusually large deviations from average career
    trajectory?
  • This helps avoid selection biases.
  • Three-year period with performance significantly
    better than career average and previous three
    years
  • Significant dispersion compared with average
    career trajectory.

21
Players with Unusual Profiles,1975-2007
  • Batters
  • 1975-1977 Rod Carew
  • 1996-2000 Ken Caminiti
  • 2000-2002 Jason Giambi
  • 2000-2002 Sammy Sosa
  • 2001-2003 Bret Boone
  • 2001-2004 Barry Bonds
  • Pitchers
  • 1986-1989 Mike Scott
  • 1993-1998 Greg Maddux
  • 1999-2003 Pedro Martinez
  • 2002-2004 Jason Schmidt
  • Baseline of 1 positive per decade prior to
    2000
  • 2000s appear to be a very different era

22
MLB Average Dispersions from Baseline
Batters
Pitchers
  • Overall, players show average tendencies.

23
Summary
  1. The player seasons implicated for steroid use in
    the Mitchell Report were better than career
    baseline at about the 2-sigma level.
  2. Large number of significant deviations from
    career baseline over last 10 years, especially
    among hitters.
  3. League-wide, players generally are within
    historical norms of baseline performance thus it
    is unlikely that a large number of players are
    achieving a significant benefit from steroid use.
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