A quick look at baserunning

You’re probably looking around going, “Where’s my roundtable?” And you will have a roundtable. Probably Friday. In the meantime, I’m laying out some finger sandwiches and lemonade – a light afternoon snack, if you’d like. Partake if you wish.

So I have a baserunning evaluation metric, measured in runs above/below average. Nothing fancy or special, really. Dan Fox has covered this ground a lot better than I have. (And that’s just the tip of the iceberg.) So here’s how I dos it:

2. Calculate run expectancy separately for each base, like this, for each season.
3. Looking only at the lead baserunner, calculate the average destination run expectancy for each event. Everything was broken down by the following categories:
• Number of outs remaining,
• Event code (single, double, out, wild pitch, etc.),
• Batted ball type,
• Whether the batter was bunting,
• Whether the ball was hit to the battery (pitcher/catcher), an infielder or an outfielder,
• Whether the ball was hit to the left or right side of the field.
4. Compare what a player did to the average.

Let’s say you have a runner on first, no outs. Most of the time a runner ends up on second, some of the time on third, when a ground-ball single is hit into left field. If a runner ends up on second, he gets a (very slight) debit. If he ends up on third, he gets a credit. All of these changes are tracked and totaled up.

Simple and easy, right? Here’s the top ten baserunning +/- seasons, 1953-2007:

 YEAR_ID PLAYER_ID Name TEAM_ID PLUS_MINUS 1965 flooc101 Curt Flood SLN 12 1976 patef101 Freddie Patek KCA 12 2004 erstd001 Darin Erstad ANA 11 1991 molip001 Paul Molitor MIL 10 1978 puhlt001 Terry Puhl HOU 10 2000 goodt001 Tom Goodwin COL 10 1987 browj001 Jerry Browne TEX 10 1974 bochb001 Bruce Bochte CAL 10 1957 blasd101 Don Blasingame SLN 10 1976 leflr101 Ron LeFlore DET 10

You’ll note that the best baserunning season of the Retroera was only worth 12 runs above average. Obviously you’d prefer a good baserunner to a bad baserunner, all else being equal, but it definitely takes a backseat to hitting and defense.

Ten worst seasons?

 YEAR_ID PLAYER_ID Name TEAM_ID PLUS_MINUS 2007 lodup001 Paul Lo Duca NYN -9 1959 thomf103 Frank Thomas CIN -9 1980 cruzj001 Jose Cruz HOU -9 1965 johnd103 Deron Johnson CIN -9 1962 brutb101 Bill Bruton DET -9 1976 sizet101 Ted Sizemore LAN -10 1974 darwb101 Bobby Darwin MIN -10 1999 stanm002 Mike Stanley BOS -10 1965 fairr101 Ron Fairly LAN -10 1964 bertd101 Dick Bertell CHN -13

UPDATE: This is too large for an EditGrid, so here’s a full spreadsheet, including career totals. Requires something that can read Excel files. Best I can do for y’all right now.

5 Responses to A quick look at baserunning

1. Brian Cartwright says:

Try grouping by run differential as well

2. Pizza Cutter says:

But he must be a good and valuable player… he’s fast!

3. Colin Wyers says:

I’m reticent to add any more adjustments the way I currently do it, Brian, because then you start to really shave down the sample sizes on the state-to-state transitions. Especially since I’m doing it season-by-season, it starts to get dangerous if you drill down anymore. I’m sure there’s a way to handle it better, but a lot more work would have to go into it.
And sportwriters say stuff like that, PC, but when it comes down to brass tacks, like the MVP award, they vote a Big Damn Slugger with no other positives second. It’s all lip service.

4. Dan Novick says:

“they vote for a Big Damn Slugger”
Like Dustin Pedroia?
I agree with you for the most part btw, I’m just giving you a hard time.

5. Brian Cartwright says:

This is an idea that I’ve actually had for close to 25 years, since I kept statistics and had all the play by play for a college summer league, but it’s still on my to-do list.
The way I have it conceptualized, if there’s a rare grouping of events, the expected value will not be as accurate because it’s based on a much smaller sample size – but, if the player’s samples are weighted by how often that player is in each situation, then the effect in the final rating of a larger variance in the expected value of any subgroup will be minimized by the weighting.
So, of any groupings that you have, calculate their expected rate (the league mean over x number of seasons). Find the number of times that a player was in each situation, and calculate the player’s weighted overall expected rate, then compare to the player’s observed rate, and convert to runs.