In the first two parts of this series, I looked at the best outfielders and the worst outfielders at preventing base runners from taking the extra base. In the third and final part of this series, I will look at who were the best and the worst base runners of 2012.
When I evaluated an outfielder’s ability to prevent base runners from taking the extra base, I used two different methods. The first method focused on the rate in which base runners took the extra base in 4 specific situations on the outfielder. The second method incorporated run expectancy into the equation by looking at the before and after state of each of the 4 situations and measuring the net change in run expectancy. To evaluate the best and worst best runners of 2012, I’ll use the almost the same methods, but from the base runner’s perspective.
The first method focuses on the base runner’s aggressiveness and his efficiency by looking at how often he is able to take the extra base without getting thrown out when the opportunity is presented. To do that, I looked at the same 4 situations that were used for evaluating the outfielders. To reiterate, the four situations are:
1.) Going from 1st to 3rd on a single
2.) Going from 2nd to Home on a single
3.) Going from 1st to Home on a double
4.) Going from 3rd to Home on a fly ball with less than two outs
I then measured how often the base runners were able to reach these bases when one of four situations above occurred. Once I found out the rate at which the base runners took the extra base, I subtracted it by the league average and then multiplied it by the number of extra bases they took. The formula is as follows
wXB= ((Extra Bases Taken/Total Opportunities)-ML Average(Extra Bases Taken/Total Opportunities))*number of extra bases taken
weighted extra base efficiency or (wXB) measures the number of extra bases the runner takes and then weights it by how efficient they are compared to Major League average. Below is a list of the top and bottom 5 of base runners in terms of wXB
The results from above mesh pretty well with general consensus on who are the good and bad base runners. In the top five we have three of the speedier players in the game in Trout, Fowler, and Andrus while the other two players aren’t necessarily known for their speed, but are rather efficient on the base path. The bottom 5 is stacked with the prototypical base-cloggers. There are two catchers (one coming off knee surgery), two large first basemen, and an older infielder who is not know for his speed. Overall the results are not that surprising, however, there are some discrepancies that are not being addressed. While wXB is a good metric that allows us to measure efficiency and aggressiveness at the same time, it doesn’t differentiate between getting thrown out on the base path and simply holding up, as well as, the fact that it leaves out the stolen base component of base running altogether.
To compensate for these components, we have to introduce run expectancy into the equation. Simply looking at how often the base runner takes the extra base does not tell the whole story. By looking at the net change in run expectancy before and after the play, we can quantify how many runs a base runner is contributing to his team’s offense. It also allows us to compare the base runner’s ability to steal bases with his ability to take the extra base on a single or a double.
The formula for this is broken down into two parts and is very simple. The first part is the base runner’s base stealing contribution. To measure this, I simply look at the run expectancy before the base runner attempted a steal (runner on 1st and 0 outs = .861) and compare it to the run expectancy after the base runner attempted the steal (successful: runner on 2nd 0 outs (1.203 -.861) = .342, not successful: bases empty and 1 out =(.268 – .861) = -.593) .I then accumulate the net total from all of the baserunner’s stolen base attempts giving us the total runs added by the base runner via stolen bases. The good part about this is that base stealers who steal a lot of bases, but get caught a lot, will be properly penalized not only for getting caught stealing, but for the context in which they were caught stealing (i.e. trying to steal 3rd with 1 out).
The second part of the equation looks at the four situations described above and measures the net change in run expectancy on those plays rather then whether or not the base runner was able to take the extra base or not. I then sum the runs added with the runs added from stealing bases so that we have a total of runs accumulated by the base runner while on the base path.Below are the best and worst of the base runners in terms of Base Running Runs Added or (BRRA)
It is comforting to see Mike Trout atop this list. Anyone who has seen him play knows that he has a propensity for taking extra bases whenever possible. Angel Pagan is somewhat of a surprise at number 2. He definitely has some speed, but it was interesting to see how well he used it to improve his team’s chances of scoring. The rest of the top 5 is not very surprising, all of these base runners are among the fastest base runners in the league and have each recorded at least 30 stolen bases in a single season.
The bottom five is somewhat of a mis-mash of players who you wouldn’t necessarily expect to be ranked as the worst base runners. Tony Gwynn in particular is known for his speed but when we look at his base running stats closely, we can see that he was only 13 out of 19 in stolen base attempts, a very paltry 68%. Ruggiano and Beckham fit into the Gwynn mold of average to above average runners who simply were thrown out far too often, while Chipper Jones and Brandon Belt are just plain slow.
This concludes my look into some of the more minute details of the game. I will be tracking these same stats throughout the 2013 season and checking to see if there is some consistency between the 2012 and 2013 seasons.