Wichita State Baseball: Comparison of Each Batter's Contribution This Season

I finished the first iteration of parsing the NCAA D1 baseball play-by-play data today. Now I want to take a look at some advanced metrics that can be used from this information. The first one I am looking at is the run contribution each batter has made to the team this season (2016).

Let me explain briefly what I mean by run contribution and how it fits into evaluating baseball players. For any given plate appearance there is a potential to score a run. We also know not every batting play is equal. An obvious one is that a home run is more valuable than a hit with no runners on. However a hit with the bases loaded could be more valuable than a home run. The play-by-play data allows us to create a run expectancy matrix for all of the possible runners on base states which is 8 and the possible number of outs which is 3 (0,1, and 2). This matrix showed me the run potential for each of those possible scenarios.

Note: This matrix is not 100 percent accurate. It is based on the my first attempt to parse the play-by-play data and seems to be slightly underestimating each state.

For example with a runner on first and no outs the average runs scored by all NCAA D1 teams for the rest of the inning where this is the scenario is - by my calculation - +.86 or about +1. If a player gets a lead-off single, that team should score on average around one run in that inning. Why does this matter? If we know the run expectancy of each state we can determine the run contribution of each batter based on how they performed in each one of those states. Knowing the run contribution of each player allows us to better evaluate the players. It let's us understand how well each batter performed in every scenario from no runners on and no outs to bases loaded with two outs. Total all of those scenarios together and you can see the total contribution of that player. Runs Created is not RBI's or Hits it is a new value generated by estimating the run contribution of each batting play of the season.

Without further ado....

Run Contribution Chart For Players With More Than 10 AB's

No surprise Troutwine tops the list with nearly 6 Runs Created. To put things into perspective. Corey Ray from Louisville University's - one of the best players in college baseball - has a Runs Created stat of around 7. The players who have negative values should not be discouraged because it is early in the season. A solid player will end with a Runs Created (RC) around 15 based on the averages I calculated from the play-by-play data from the 2015 season.

As you can see from the chart, a high or low OPS (OBPct + SLGPct) does not necessarily mean that player is contributing more or less than another player. For example, a player who hit ten triples in ten at-bats with the bases empty would have a very high OPS and a decent Runs Created value. On the other hand, a player who hit ten doubles in ten at-bats with the bases loaded would have a lower OPS than the previous player, but a higher Runs Created. Obviously this is an extreme hypothetical, but I was trying to make a point. Runs Created goes beyond the triple slash to paint a more accurate picture of the contribution the team is getting from each batter.

As I mentioned above, the RC stat can be used to see how well batters performed in each type of situation such as with the bases loaded and so on. Below are visualizations of this exact measure for all of the players listed in the chart above. Notice the line at the zero mark in each chart. Points above the line represent positive run values contributed for each base state. Points below represent negative run values such as when the batter makes an out or performs some other type of low level outcome like being hit by the pitch or sac bunt. The more points above the line the better. Please note you may not be able to see all the points as some overlap each other. This can make a few of the charts looked skewed.

In the next post I will use this research along with other baseball statistics and research to create an optimized lineup for Wichita State. Preliminary analysis looks like Shocker baseball could be slotting guys in the order differently to achieve better results over the course of the season. As it stands guys are being placed in the batting order where it is not maximizing their individual benefits.

Kirk

DeBacker

Bohm

Dugas

Eaton

Jenista

Mucha

Rader

Reding

Ritter

Tinkham

Troutwine

Vickers

Young

Batters' Salaries After an All Star Year Compared to Their Production

This post goes along with a previous post in which I found that "...an All-Star selection can increase a player's salary significantly; on average by $1,517,550." Read the previous post here. What I did not demonstrate in the previous post, but intend to explain in this post is how that increase in a player's salary the year after an All-Star team selection relates to their statistical performance in that same year.

I used the same data that I compiled for the previous post, which included the list of All-Stars from 2002 - 2013 and their salaries before and after they were selected to the All-Star team, and added those same players core season statistics. For this post I am only going to deal with the batters, therefore I deleted the pitchers from my dataset as to not skew the calculations.

What I found was somewhat alarming, but not all that surprising. As I mentioned before, a player's salary increases by about $1.5 million on average the year after they are an All-Star. When I eliminated the pitchers from this list I found that batters ordinarily see an increase in annual salary of $1,426,104; a little less, but still a significant increase. As you look at the table, you will see that although teams tend to pay their batters more for making the All-Star team they do not necessarily see an increase or even a sustained level of production the following year from those players.



With that being said, in the grand scheme of things a drop off of an average of 18.1 Runs Created from one year to the next could be nothing more than simple regression. There will be years in which a player over performs for whatever reason. This does not mean the player is "overrated" or that he won't have another good year in the future. It simply means that if a player is a consistent .750 OPS guy and he has a year in which his OPS is .820 it's safe to say the next season he would be due to regress.

Also, I did not include age in my query of this dataset, but it should not be ruled out. If I'm not mistaken, with exception to perennial All-Stars (i.e. Jeter), players tend to make the All-Star team during their prime year(s) meaning 25-28 year old. As a whole, this slight drop off in production might be nothing more than age taking its toll.

So back to the question I posed in the previous post on whether an increase in a player's salary for making an All-Star game is justifiable for teams based on the future production of that player. At least with regards to the All-Star batters I would have to say no. Even though the overall average decrease in Runs Created was minimal, 18.1, it was still a decrease. I'm not saying don't reward the player at all, but it seems much more suitable in this case for teams to pay one time bonuses to players instead of increasing their annual salaries. Why pay a player a higher salary and ultimately shrink your payroll that much more for a guy who performed well for 90 games or so? This allows more risk to creep back into a team's decisions. I say, leave the salary raises for current players that prove they can consistently perform at a high level for multiple seasons or save the money for free agent acquisitions.

The way the system works right now, a player can take advantage of a half seasons worth of good games or even a strong marketing campaign to elevate their annual salary by $1.5 million. Teams that have to scrape every penny to obtain and keep good players should pay close attention to these kinds of findings so they are not poorly allocating scarce resources. Its Economics 101.

(SQL query of Lahman database using the Salaries, Batting, and AllStarFull followed by data manipulation in R studio)