The AL is actually much easier to deal with because there is no “Barry Bonds” factor. Regardless, the formula has been changing daily, and after some thoughtful and sensible analysis I’ve arrived at the conclusion that voters are not consistent evaluators of MVP candidacy. There are relationships to be found between the distribution of voting points and the metrics that we use to gauge player performance, but that is only because there are only about five players each season that could even be considered. From there the selection of the ultimate winner is mostly driven by the motives of the people voting, and where their loyalties lie (See 2006 AL MVP). To elucidate this concept, I created a graph showing WAR for each winner and average WAR for the top 5 since 1990. Now obviously I wouldn’t expect a straight line from left to right, nor a steady increase. The concept being elucidated is not one to show fault of the voters, but of the unpredictability of how voters view an MVP winner. It appears to change from year to year.
At first glance, one might think this simply is a representation of fluctuating talent. The statistic itself adjusts for league wide scoring trends for each particular season, and with each team having access to the diverse international talent pool, the average bio-mechanical limits of players are at a league-wide equilibrium, and have been since the talent pool expanded decades ago. Other than the steroid jerk from around 1998-2004, player ability, as betrayed by the left side of the graph above, hasn’t increased nor decreased drastically in any given year. The year 2000 appears to be the only anomaly on the graph, steroids notwithstanding, and Pedro Martinez and his ridiculous 10.3 WAR (4th MVP) is enough to explain the spike. Stephen Jay Gould would be proud.
Statistics are becoming more and more sophisticated, and writers/bloggers are doing whatever they can to appear more sophisticated. Thus many of them have
embraced adopted WAR among other saber-stats. Because of this general propensity, I anticipated the lower WAR values for MVP winners to be from the 90s. To some degree this is true. Dennis Eckersley won the MVP in 1992, with a WAR of 3, outstanding for a relief pitcher (WAR is a counting statistic, so relievers have lower WARs by default). And Bill James will be happy to know swing-happy Juan Gonzalez has the lowest WAR for any MVP winner since 1990, at 2.8. But there is nothing else one can take from the graph other than randomness. Even the two highest WARs are from 1990 and 1991, Henderson and Ripken respectively. Obviously I didn’t expect with the creation of WAR comes an overall increase in player ability, which is just silly. I don’t know what I expected. Though it seems I should increase my sample size to span those years dating back to 1990, and probably further, rather than only using the last eleven seasons. Having said that, the current formula correctly selected eight of the last eleven MVP winners, so all the extra effort would probably be wasted energy. I’m only doing this to find value.
As I said in the previous post, I separated the MVP candidates into three groups: hitters, starting pitchers, and relief pitchers. This should be obvious enough, as the metrics used to define the best players in each category are drastically different.
I had been entertaining the idea of including WPA (Win probability added). Intuitively it makes sense that WPA is strongly linked to standard measures of offensive ability (AVG, HRS, RBI), as most events within a game occur when the run differential is within three runs. However, pitchers aren’t always in control of their statistical fate. At the same time WPA is taken directly from each individual event. Imagine a starting pitcher up 3-2 in the 7th inning with two outs leaving the game with runners on first and second. His replacement promptly surrenders a three-run HR. Two runs are charged to the starting pitcher, hurting his ERA, and he is now in line for the loss. At the same time, his WPA has not changed from another pitcher’s event. The last measurement taken for his WPA was whatever occurred with the batter before being replaced. Because of this, there is a conspicuous asymmetry in the relationship between raw statistics and WPA.
Obviously there are situations when hitters could see an increase in WPA while seeing a reduction in AVG, perhaps due to an error by the defense. But the impact is not as severe.
Team wins is another variable I had used, but are team wins indicative of voting trends or merely a by-product of the best players playing on the best teams? If I replace team wins with just a binary appropriation of playoff outlook (0 for no, 1 for yes), the table is more in agreement with intuition while possessing similar descriptive statistics. Take a quick gander at the last MVP update and you’ll understand why I replaced team wins with a yes/no playoff variable. Human thought can occasionally outwit statistics, as long as it suits one’s agenda.
Batter: Playoff, WAR, WPA, BA, HR, RBI, C
Pitcher: Playoff, WAR, WHIP, W%, C
The variables above represent a trend from 2000-2010, therefore some statistics, like ERA, do not translate to voting points to a certain degree. On a couple of occasions, a pitcher with a 4+ ERA received voting points, and the only reason WHIP is included is due to its lower overall variance. Nonetheless it works much better in this particular formula, and I can’t control what the voters decide. Again, I’m trying to find value based on historical data. Those who think Verlander is too low consider I only used data from 2000-2010, which didn’t see any pitcher win the MVP. If/When I include seasons dating back thirty years, Verlander’s odds may increase slightly.