Cold snowy days here in our nation’s capital, combined with the owners’ and players’ seeming determination to kill the golden goose, provides an opportunity for me to look at the hot stove (pre-lockout) through the lens of the Peta methodology. For those unfamiliar with the Peta methodology, I refer you to this deeper dive here on the Community Blog published last January. Based on Joe Peta’s groundbreaking 2013 book Trading Bases, the methodology derives each team’s upcoming season win-loss record based on the utilization of its previous season performance (runs scored/runs allowed), adjusted for cluster luck (my proxy is FG BaseRuns), and the team’s upcoming-season projected WAR.

Just before Opening Day, the product of this calculation is compared to the money line. Peta suggests that in a 162-game season, win totals produced by the model that deviate from the money line by more than four games (1.5 games in a 60- game season) represented “unrepeatable results” and therefore were worth a possible wager. Read the rest of this entry »

For those unfamiliar with Joe Peta’s groundbreaking 2013 book Trading Bases, the author is a successful financial analyst and former Wall Street trader. Seriously injured in a traffic accident, Peta’s long and painful recovery included employing his professional skills to develop a baseball wagering methodology. His book is about more than that though, including observations about the 2008 economic meltdown and sports wagering writ large. Peta’s anecdotes alone make it worth the read — imagine being hit by a NYC ambulance and then being billed by the city for the ride to the hospital.

At its highest level, the Peta methodology is based on the utilization of a team’s previous season performance adjusted for cluster luck (a regression of OBP/SLG/ISO to arrive at “hits per run”) and WAR, as well as upcoming-season projected WAR. Arriving at an estimate of a team’s season win total, it is then used to identify and capitalize on inefficiencies between the model’s estimates and wagering lines.

Peta’s work produces two products: a season-long projection of wins (the long game) and the ability to handicap individual games through adjustments to each team’s lineup, starting pitcher, and home field. While conceptually straightforward, it is time-consuming to operate, requiring familiarity with Excel (particularly the ability to link sheets). In lieu of Peta’s regression calculation of cluster luck, I utilized FanGraphs’ calculation of BaseRuns, convinced of its utility as a proxy after reading a 2019 article at samkonmodels.com arguing it was one of a number of comparable and readily available such calculations. Read the rest of this entry »