Spring Synopsis: Mariners to win 111 games

The M’s have put together a decent spring, and even after yesterday’s loss they find themselves at 11- 5. It’s easy to get excited about players and teams performing well in Spring Training, but how much weight should be placed on these statistics? After all, Seattle and San Diego found themselves near the top of the Cactus League standings last season, and then they finished the regular season with only 75 and 76 wins, respectively. And going into Sunday, Carlos Peguero had a .400 batting average. His over-under line for the regular season sits at about Mendoza.

With spring stats, there are some things that matter, and some things that just don’t. Felix Hernandez’s fastball velocity relative to last spring probably matters, Carlos Peguero’s .400 batting average almost certainly doesn’t, and the Mariners 11 – 5 record? That probably doesn’t matter either.

Over the past three seasons, spring records by themselves have only been able to explain about 4% of the fluctuation in regular season records, leaving 96% of the explanation vulnerable to the cruel intentions of lurking variables. The average error—or distance between the regression line and the actual winning percentages—was plus-or-minus 9 wins. That’s like me saying, “the M’s will probably win between 70 and 88 games this season.” Not really that helpful. Maybe the graph shows best how terribly spring predicts summer…

Spring Training Regression

It’s not hard to reason why spring records don’t matter that much. First, consider the lineup the M’s put out yesterday. Of the team’s 39 plate appearances, only 6 went to opening day starters (Kyle Seager and Michael Morse). Second, look at Felix’s pitch selection during his first start last Thursday. He threw 63% changeups (and only pitched two innings). 63 percent?! That’s not a pitcher trying to win games for his team right now; that’s a pitcher getting ready to win games in May and June.

To be clear, being 11 – 5 is better than 5 – 11. But records from the spring come from a small sample, and they come from a strange sample—a sample where Felix pitches only changeups and Peguero gets to hit off the same guys he does in AAA. Things that happen during March don’t erase what players have shown us before. In fact, we should probably pay more attention to those other things from before.

There are some stats at an individual level that might mean something. Quick-stabilizing statistics like a batter’s swing rate or a pitcher’s velocity, for instance, could be the result of a change in approach or a natural aging process. And they might stabilize fast enough to be meaningful in the spring. But even statistics that are important during the regular season, like OBP and Slugging, mean very little in the spring, so we have to be careful at which stats we look. I quote the following from some research I blogged about this time last year:

I took at a look at some spring training stats over the last three seasons to see if they were at all predictive of regular season performance. I took only the players who racked up enough spring at bats to be in the top 20 each of the last three spring seasons. In other words, these are the guys with the largest spring sample sizes. First, I looked at the basic linear correlation between spring stats and season stats for batting average, OBP, slugging, K-rate and walk rate. The only correlations that were even significant came in strikeout rates and slugging percentage, and they weren’t all that strong (r = .33 and .48, respectively).

For every Miguel Cabrera (who slashed .356/.397/.603 and followed it up during the season with an equally productive .328/.420/.622) there were two Chone Figgins (Figginses?) who teased us with a .373/.448/.490 line. I don’t need to remind us what he did during that season. Spring training is experimenting time. Managers experiment, hitters experiment, pitchers experiment. But the real driving force behind the lack of any helpful, predictive spring stats is a small sample size.

So as fans, we should take spring ball for what it is. A chance for players to gear back up for the season, and a chance for us to watch them do it without having to worry about falling below .500. But there is not a lot to be taken from most of the readily available stats. Someone is going to unexpectedly scorch the ball this spring for the Ms. It might even be Chone Figgins [but not THIS year!] We should only care because it’s fun to watch he who’s made us suffer play well, not because it foretells of any career revivals.

And that’s a fitting end for this post, as well. I’m sorry I lied to you in the title; the M’s are not likely to win more than 100 games this season. Spoiler alert!

If you want some real projections, take a look at Dan Szymborski’s ZiPS projections. It’s good stuff!

  • http://twitter.com/CaseyMcLain34 Casey McLain

    OOOOO AHHHHHH Dots and Lines!

    • Matthias_Kullowatz

      This is like the porn of statistics.

  • http://twitter.com/MGVernon M G Vernon

    So what’s your projection for the seasons wins and losses?

    • Matthias_Kullowatz

      I’ve been meaning to piece together the ZiPS and Steamer player projections for each of the five AL West teams and make some team projections, but just looking at run differentials from last year and changes to the roster, I have been thinking 75 – 80 expected wins, with the real possibility of extremes as wide as 65 – 90. Variance is crazy in baseball, even in 162 games, as the Orioles exhibited last season.