My high-school-aged son sits at home yet again. Why? Because another of his baseball games has been canceled due to the wet and cold Ohio spring, and my thoughts turn again to our days playing baseball in Florida. Before we moved to this less-agreeable northern climate, it was a rarity to have a game canceled due to weather. Not only that, but games were scheduled year-round, which of course meant more baseball on the calendar. This situation reminded me of the familiar equation known to baseball fans:

Good weather leads to more playing.
More playing means better players.

But is this true? After all, it’s well-known that the best player in baseball, Mike Trout, is from cold-weather New Jersey. Many quickly point to the fact that California, Texas, and Florida are at the top of the list for states with the most MLB draftees, but they’re the three most populous states. Perhaps proportionally they don’t stack up to colder states after all.

I decided to look at the data from the last two drafts — 2017 and 2018 — to see if there is a relationship between a state’s average temperature and how well its players do in the draft. Do warmer-weather states really produce more MLB draftees than average?

To do this, I first gathered population data from each state to determine what percentage of the overall US population it contains. Then I did the same for each states’ MLB draft population. Finally, I compared those two figures and determined the percentage difference between their population proportion and their draft proportion. I call this figure the “Draft Difference”.

For example, let’s say State X makes up 10% of the US Population, but the State X’s draft class makes up only 8% of the overall class. Its Draft Difference is calculated as:

(Draft-Population)/Population = Draft Difference

In this case,

(8-10)/10 = -.20 = -20%

A state with 10% of the US population should, all things being equal, contribute 10% of all players in an MLB draft. But, in this case, State X did 20% worse than should be expected just from its population size. Read the rest of this entry »

Baseball fans have seen it time and again: a starting pitcher will twirl a masterpiece, but because his team doesn’t score, he’ll be tagged with a loss. Or a reliever will come into a game, pitch to one or two batters, and end up with the win.

The vagaries of assigning wins and losses to pitchers are a well-known irritant to serious baseball fans (though perhaps not to old-timers like Bob Costas or John Smoltz). Here is the pitching decision statistic explained:

The winning pitcher is defined as the pitcher who last pitched prior to the half-inning when the winning team took the lead for the last time.

The losing pitcher is the pitcher who allows the go-ahead run to reach base for a lead that the winning team never relinquishes.

Often timing — particularly the timing of a team’s offense — affects the statistic more than a pitcher’s actual contribution to his team’s win or loss. In other words, the decision frequently fails to reflect which pitcher made the biggest difference for the winning team (or was most detrimental for the losing team). In these cases, it simply tags the pitcher lucky or unlucky enough to pitch at a certain time in the game.

In an effort to create a more accurate stat to reflect a pitcher’s contribution to his team’s win or loss, I’d like to propose new stats, which I’ll call the “WPA-Win” and “WPA-Loss.” Let’s start with the WPA-Win:

The “WPA-Win” is given to the pitcher on the winning team with the highest WPA for that game.

I’ll address how to calculate the “WPA-Loss” (which is more complicated) later in the article. For now, we’ll just assume it goes to the pitcher on the losing team with the lowest WPA. Read the rest of this entry »