January 21, 2015
Quantifying the Wobbly Chair
Last fall, the Diamondbacks, Cubs, and Red Sox all finished last in their respective divisions. The Diamondbacks dismissed manager Kirk Gibson in what was widely seen as an appropriate move given the franchise’s decline and Gibson’s grittiness-bordering-on-violence. The Cubs fired manager Rick Renteria, not because of performance but because Joe Maddon became available. Public reaction was one of uncomfortable sympathy; nobody was out for Renteria’s head, but c’mon, it’s Joe Freaking Maddon. The Red Sox retained John Farrell, whose team severely underperformed expectations. Surely he benefitted here from a wildly successful 2013.
Point being, keeping or dismissing a manager is a complicated decision, in which on-field results have to be weighed against history, context, and intangibles like leadership and respect. But of the tangible results, which types truly matter, and how much does each shade the picture? I aimed to build a model to answer that question.
Across various re-samplings and variable sets, test-set predictions were about 86-87 percent accurate—reaching as high as 91 percent in one instance—compared to a base return rate of 76.5 percent. For the final model, however, I removed variables “# of Games,” “Wins,” and “Losses,” not because they weren’t helpful, but because they were too helpful. Using either “# Games” or a combination of “Wins” and “Losses,” the model can predict without fail that mid-season firings won’t return next year. This is good for prediction accuracy, but the result is that other, more interesting variables got drowned out. The final model still predicts about 84 percent accurately and is far less simplistic.
The gbm R package spits out a nifty summary of the percent influence of each variable. For clarification, the “2-year playoffs” variables indicate which of the current and previous year the team made the playoffs. Options are “Both,” “Neither,” “First” and “Second.” The 2014 Red Sox would be “First” since they advanced in 2013 but not 2014. Here are the results:
It’s important to note that many of these variables are related to each other, which can have a big effect on their levels of influence. For example, making the playoffs doesn’t actually have zero influence on a manager’s job security, but other variables like “Win %” and “Divisional Ranking” were more helpful and subsumed the influence of “Made Playoffs.” Similarly, winning the World Series has “zero influence” only in the sense that by the time you’re there, you’ve already solidified your position, and other variables catch that. Also, since the model looks at all kinds of departures and not just firings, variables like “age” or “years with team” gain importance, as they can indicate whether a manager is likely to retire.
Examining the variables shows a few interesting results. For example, you’re apparently better off having a .400 winning percentage than .450. I’d venture to guess that the .400 teams were known to be terrible from the start, whereas the manager takes some flak when his pre-season dark horse wins only 73 games. Also, winning the wild card appears to be negatively correlated with return. This is probably not a true phenomenon. It’s much more likely that the model is using the wild card variable to separate strong finishers from teams who lost their division lead down the stretch, or something along those lines.
Okay, that’s enough explanation. It’s time to announce the Managerial Model Awards, given only to the most notable managerial seasons of the wild card era.
Most Slam-Dunk Return
Runners-Up: Ron Gardenhire (2002 Twins), John Farrell (2013 Red Sox)
Most Slam-Dunk Departure
Runners-Up: Davey Lopes (2002 Brewers), Frank Robinson (2006 Nationals)
Most Surprising Return
Runners-Up: Frank Robinson (2004 Expos), Bud Black (2011 Padres)
Most Surprising Departure
Runners-Up: Davey Johnson (1997 Orioles), Larry Dierker (2001 Astros)
Flip a Coin