A Deeper Look at Baserunning, Part 1

I have previously posted details of my analysis of the "Little Things" in baseball (here and here): The aspects of the game (such as directional hitting and baserunning) that do not show up in a box score. Based on that analysis and on some interesting comments, I decided to take a deeper look at the baserunning aspects of the "little things".

Once I got into this, I immediately saw the need to split the article into three pieces:

  1. Team Baserunning
  2. Individual Baserunning
  3. Comparison to Baseball Prospectus Baserunning Statistics

This is the first of three articles, and focuses on team-by-team baserunning. The full team by team spreadsheet is posted on Google Docs here. I broke out baserunning into five categories and analyzed expected runs for each category.

  1. Stolen Bases (positive)
  2. Caught Stealing (negative)
  3. Extra Bases (positive) - The runner takes an extra base after a batted ball (hit or out)
  4. Stay (negative) - Runner does not take the extra base.
  5. Out On Base Paths (negative) - Runner is thrown out trying to take the extra base. 

I'll go into more detail after the jump, but a few of the interesting findings are below. 

  • Philadelphia was by far the best baserunning team in the majors.
  • Minnesota was much better at "non-SB" baserunning (3 through 5 above) than at stealing bases.
  • In general, teams hurt themselves attempting to steal bases.
  • Minnesota was the most aggressive baserunning team in the majors, and they led the league in "extra bases added".

 

Stealing Bases

Expected runs gained or lost due to stolen base attempts are labeled "ER_ALLSB" in the spreadsheet. Philadelphia led the majors, followed by the two New York teams. 

Team ER_ALLSB
Philadelphia Phillies +3.18
New York Mets +1.70
New York Yankees +1.63

Baltimore trailed the pack, with Cincinnati and Kansas City right in front of the Orioles.

Team ER_ALLSB
Baltimore Orioles -11.86
Kansas City Royals -11.56
Cincinnati Reds -11.54

Not surprisingly, Philadelphia led the majors with an 81.9% SB success rate. Kansas City and Baltimore trailed the rest of the league, swiping at 61.3% and 61.9%, respectively. While there is a clear correlation between SB success rate and expected runs, it is not a perfect correlation. Consider Minnesota and the Chicago White Sox.

Team MLB Rank ER_ALLSB SB %
Chicago White Sox #20 -7.97 63.3%
Minnesota Twins #24 -9.94 68.5%

Minnesota had more stolen bases (98 to 62) and a better SB success rate. Why was Chicago 2 runs better stealing bases? In order to answer this, we need to consider context. The number of expected runs gained or lost differs based on situation. Consider a simple SB situation, a runner on first.

Outs Result ER_BEFORE ER_AFTER GAIN_LOSS REQ_SUCC_RATE
0 SB 0.936 1.191 +0.255 71.9%
0 CS 0.936 0.283 -0.653 71.9%
1 SB 0.591 0.719 +0.128 78.9%
1 CS 0.591 0.112 -0.479 78.9%
2 SB 0.277 0.351 +0.074 78.9%
2 CS 0.277 0.000 -0.277 78.9%

From a risk-reward perspective, the expected run penalty for being caught stealing with no one out is much greater than with one or two outs, but the reward is greater. In fact, in order to break even from an expected runs perspective, a team only needs to steal at a 71.9% clip with no one out versus 78.9% with one or two outs and a runner on first. Chances are, the Twins were caught stealing in higher penalty situations (or stole in lower reward situations) than the White Sox. 

From an expected runs strategy standpoint (and this has been pointed out by many before me), stealing bases is a losing strategy. 24 out of 30 MLB teams lost runs due to stolen base attempts, and across the league, each team lost an average of 5.6 runs compared to a team that never attempted a stolen base. Of course, this is a simplistic, first order analysis of expected runs and does not consider a team's increased chances of scoring at least one additional run (useful late in a close game) or a base stealer's effect on rattling pitchers. Until I look at these second order effects, I am not prepared to make this a "stolen bases are bad" article...an interesting finding though.

"Non-SB" Baserunning

This is the aspect of baserunning that doesn't appear in the box score, and includes a runner taking the extra base (first to third on a base hit, home on a sac fly, etc.) I break down "non-SB" baserunning into three areas.

  1. Extra Bases - The runner advances beyond a "default" number of bases (zero for an out, one for single, two for double, etc.). A portion of the runs is allocated to the batter based on the league wide extra base rate for a given hit type and location.
  2. Stay - The runner fails to advance an extra base, staying at his current base. The runner is penalized the same number of expected runs as the batter was assigned based on league wide rate.
  3. Out On Base Paths (OOBP) - The runner is thrown out trying to advance an extra base.

Again, Philadelphia led the majors, followed by Minnesota and the Angels. Comparing Minnesota and Philadelphia, the Twins created about 22 more runs by taking extra bases, but they gave up about 10 and 15 net runs due to staying on base and being thrown out on the basepaths, relative to the Phillies.

Team ER_NONSB
Philadelphia Phillies +17.16
Minnesota Twins +13.57
Los Angeles Angels +13.28

San Francisco was worst in the majors, followed by Boston and San Diego. Boston was a bit surprising, but they were a relatively station to station team (with Manny, Papi, Lowell, Varitek, I'm not too surprised), one of the least aggressive in the majors.

Team ER_NONSB
San Francisco Giants -13.23
Boston Red Sox -11.52
San Diego Padres -9.19

 

Overall Baserunning

Overall Baserunning is simply an addition of SB and non-SB runs. Philadelphia was by far best in the majors, followed by the "peloton" nearly 15 runs (1.5 marginal wins) behind. Davey Lopes is a baserunning genius!

Team ER_TOTAL
Philadelphia Phillies +20.34
Colorado Rockies +6.91
Los Angeles Dodgers +6.88

Minnesota came in 7th at +3.63, above average but dragged down by their below average base stealing.

Bottom 3:

Team ER_TOTAL
San Francisco Giants -24.47
Baltimore Orioles -18.44
Chicago White Sox -15.93

Expected Run Value Weights

If one wanted to assign run value linear weights for each of the five baserunning events (similar to wOBA for 1B, 2B, etc.), I've averaged over all teams for the season.

Event Expected Runs
Stolen Base +0.151
Caught Stealing -0.487
Extra Base +0.181
Stay on Current Base -0.066
Out on Base Paths -0.671

What do these weights tell us? Based on the relative ER weights for SB/CS and EB/OOBP, a baserunner would need at least a 76.3% SB success rate in order to break even, and a runner should advance the extra base as long as there is at least a 78% chance of success. Also, the value of taking an extra base on the basepaths is actually more valuable than the average stolen base. This makes sense, considering that most stolen bases are of second base, while extra bases are typically third base or home.

Aggressiveness

In order to assess how aggressive a team is in taking the extra base, I looked at two measures. How often does a team try to take the extra base? And how many extra bases did a team take relative to league average (think +/-)?

Minnesota was the most aggressive team in the majors last year, taking a total of 661 extra bases (Texas was second with 605). The Twins attempted the extra base 44.7% of the time, tops in the league. Minnesota's directional hitting (explained in more detail in my "little things" articles) played a role in the number of extra bases taken, but aggressiveness also played a role.

Team Extra Base Attempt %
Minnesota Twins 44.7%
Los Angeles Angels 44.0%
Tampa Bay Rays 43.3%

Bottom 3:

Team Extra Base Attempt %
San Diego Padres 36.6%
Seattle Mariners 36.9%
Boston Red Sox 37.0%

There was a total spread of 8.1% between best and worst in the majors. This may not seem like a large spread, but over a league average 1428 extra base chances per year, it's 115 missed opportunities, roughly 3 out of every 4 games.

"Expected Extra Bases" and "Extra Bases Added"

In order to answer the question of how much a team improved (or hurt) itself taking extra bases compared to a league average baserunning team, one needs to consider the "expected" extra bases that would be taken by an average team. To do this, I used the league wide averages that I use to allocate expected runs to batters. If a runner is expected to advance 25% of the time for a given hit type and location (league average), the batter gets credit for 25% of an extra base, and the runner is assigned 0.25 "expected extra bases". Adding all of these expected extra bases allows me to define a baserunning "+/-". Best and worst in the majors are:

Team Extra Bases Added
Minnesota Twins +52.83
Los Angeles Angels +39.26
Tampa Bay Rays +32.82

Worst:

Team Extra Bases Added
Chicago Cubs -44.14
Boston Red Sox -34.00
Detroit Tigers -21.58

Outs on the Base Paths

Of course, a team could maximize its extra bases by always attempting the extra base (no matter how dumb), but a smart baserunning team (let's call them the "Phillies") increases its extra bases without committing more outs on the base paths. "Non-SB" expected runs captures this in runs, but what about overall outs made on the base paths (not including caught stealing)?

The Royals, Rays and Angels ran themselves out of the most innings in baseball last year. Minnesota's 64 OOBP wasn't much better, good for 5th most in the majors.

Team OOBP
Kansas City Royals 69
Tampa Bay Rays 68
Los Angeles Angels 67

And those pesky World Champions were at the bottom of the list.

Team OOBP
Florida Marlins 41
Chicago Cubs 43

Washington Nationals

Philadelphia Phillies

44 (tie)

 

Next Steps

Part 2 of this series will focus on individual players and their baserunning contributions, positive and negative. Who was the best? Who was the worst? Who are the most surprising at the top or near the bottom?

Finally, Part 3 will focus on comparing these baserunning stats to those of Baseball Prospectus, explained quite well by Sky Kalkman of Beyond the Boxscore here. BPro has a number of similar statistics, and while a number of the stats appear to correlate, there are a number of differences that I should investigate.

 

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