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FanGraphs compiles a comprehensive base running statistic they call BsR, which stands for Base Running Runs. They describe it as an “all encompassing” metric that “turns stolen bases, caught stealing and other base running plays into runs above and below average.” Examples of other base running plays include taking extra bases and being thrown out on the bases.
The range of BsR for teams right now goes from +6.1 (Dodgers) to -7.2 (Padres).
At this moment, the Reds rank 29 out of 30 teams in BsR (-6.3).
What should we make of that?
Contrary to what intuition might suggest, speed does not necessarily translate into positive base running value. So far in 2019, there is virtually no relationship between team’s average sprint speed and their BsR. The fastest team (PHI) is 9th with a BsR of +2.4. The 4th fastest team (HOU) has a BsR of -4.5, and the 4th slowest team (STL) is near the top of the league with a BsR of +6. While those teams are cherry picked for effect, even when considering all 30 teams, the coefficient of determination (also known as R2), is 0.0005. Again, virtually zero.
If speed is not the end-all-be-all for base running success, then clearly strategy and situational awareness are a key factor. Knowing when to push for extra bases or attempt a steal is a crucial aspect of having runners in the right place at the right times. Fortunately, we can better understand these situations with run expectancy data known as RE24.
The statistic RE24 provides the run scoring expectancy for all 24 base-out states. A base-out state refers to any game situation in terms of base runners and the number of outs. From bases empty and two outs (lowest run expectancy) to bases loaded and nobody out (highest run expectancy) and everything in between, we can identify how many expected runs a certain situation should produce. In turn, we can see when it does and does not make sense to try to be aggressive on the base paths.
To start, let’s look at the RE24 table that lists run expectancy by base-out state. This is based on the 2018 run scoring environment.
For an example of how to use this table, we can start with the ever-heated debate about sacrifice bunting. Old school baseball people swear by it, but do the numbers back it up?
Let’s assume there is a runner on first base with zero outs. The manager calls for a bunt to get the runner into scoring position with one out, while also taking the double play out of the picture. Get ‘em on, get ‘em over, get ‘em in, right?
Not so fast. That play actually decreases the expected runs scored for the inning. With a runner on first and no outs, the team could expect to score 0.884 runs. With a runner on second base and one out, the expected runs drop to 0.693 runs. This can change slightly depending on who is hitting (i.e. pitchers are generally better off sacrificing) but ultimately gives us a good framework of which situation is most advantageous.
With that intro to RE24, we can pivot back to base running and identify which decisions are most advantageous in terms of run expectancy. One thing to keep in mind is that with only three outs per inning, any risk of making an out will greatly decrease the run expectancy. If these decisions were made in a vacuum, it would rarely make sense to steal a base or risk-taking extra bases. But even with all the numbers and science that have really changed baseball over the past two decades, there is still an art involved that involves taking calculated risks.
Starting with single runner situations (stealing 2nd or 3rd, stretching a single/double to a double/triple), we can see two plays that stand out. The below table lists each potential play, the amount it would increase the run expectancy (RE) if safe, the amount it would decrease run expectancy if out, and the variance between the two.
In terms of the least to lose, trying for second base with two outs is easily the best option. A successful attempt only increases the run expectancy by 0.102, but there is also not much risk if the runner is caught since there are already two outs. Of the Reds 18 times caught stealing this year, 5 of them were attempting to steal 2nd with two outs.
On the other end of the spectrum is advancing to or stealing 3rd base with no outs. As the saying goes, never make the first or last out at third. Making the last out at third may not be the worst thing in the world, but making the first out definitely is. An unsuccessful attempt decreases run expectancy by 0.868, nearly an entire run.
The Reds have made this mistake one time this year, on a Jose Peraza bunt attempt that was thrown away by the defender. Peraza got greedy and tried to advance two bases on the error but end up costing his team much more than he would have gained. This is currently the worst base running decision the team has made this year, dropping the run expectancy from 1.137 to 0.269.
Moving on to base-running with multiple runners, we see the risk go way up. It is much more advantageous to keep two runners on base rather than risk an out by trying to steal or advance.
The situation with the least risk involved is advancing from first to third on a single with two outs. The potential gains are only 0.057 expected runs, considering the current runner would already be in scoring position with two outs. But the decrease in run expectancy is also much less, because, again, there are already two outs. The Reds have been caught in this situation once, with Yasiel Puig getting caught stealing third with a runner on first and two outs. The worst part of this decision was that Nick Senzel was at bat, taking a good hitter out of a scoring situation.
The worst decision with two runners involved is either stealing 2nd or stretching a single to a double with a runner safe at 3rd and one out. Despite taking the double play option away with a successful attempt, there is much more to lose if unsuccessful. The real takeaway here though is that base running mistakes with multiple runners on base will really hurt a teams run expectancy.
Finally, the grouping of situations below involves sending runners home vs holding the runner at 3rd base.
The first thing to note here is that the runner scoring is factored into this analysis for the increased run expectancy. For example, a single that scores a runner from 2nd with no outs only increases the run expectancy by 0.091 because the run expectancy with runners on 1st and 3rd and no outs is 1.793. Having a runner on first with no outs gives us a 0.884 run expectancy, so the equation becomes:
1 [if runner scores] + 0.884 – 1.793 = 0.091
It may seem counter intuitive, but the data shows that a team is projected to score almost the same number of runs, despite the runner already having scored in the safe scenario. That really goes to show how valuable that first out is. That is something the Reds could certainly improve on, with five key base running outs coming with no outs in the inning.
When looking at scenarios with the most to gain, sending a runner home with two outs is the only situation where the RE24 increase is greater than the decrease. The run expectancy with two outs is never greater than one, so any time you can get a run with two outs, it is well worth the risk.
In the case of the Reds, they have been thrown out twice trying to score from 2nd base with two outs, which, despite being unsuccessful, are decisions that you have to live with.
Going back to FanGraph’s BsR statistic, we can see who exactly on the Reds has been contributing the most negative base running value and which specific plays it is stemming from. The main culprits are:
- Eugenio Suarez -3.0 BsR
- Jose Iglesias -1.6 BsR
- Jesse Winker -1.3 BsR
- Scott Schebler -1.3 BsR
Suarez has been involved in some of the worst base running plays of the year. The first was on a single with one out where he tried to advance from first to third. This would have added 0.277 to the run expectancy, but instead decreased it by 0.724. The second was trying to stretch a single to a double with two outs and another runner safe at 3rd. This would have only added 0.076 expected runs, but with the out, Suarez detracted 0.504 expected runs. Lastly, he was involved in one of the outs at home plate. While there were two outs, making it a justifiable risk, the lack of execution still cost the Reds expected runs.
Another huge issue with not only this group of players but the Reds in general is their stolen base efficiency, or inefficiency for that matter. The generally accepted success rate required to add value through stolen bases is 75%. The four Reds players above are 2/9 on stolen base attempts, totaling an abysmal 22% success rate. The team as a whole is a bit better at 25/43 or 58%. However, that is still nowhere near the success rate to justify attempted steals.
One silver lining here is that an overwhelming number of failed attempts have come trying to steal second with one or two outs, which are not as bad risks to take. It is hard to say exactly how much luck goes into base running, but based on the run expectancy numbers and the Reds track record this year, they are not so much failing due to taking crazy risks, but rather they are failing to execute on less risky decisions.
