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Best Shape of His Life! Does the Oft-Mocked Spring Training Declaration Actually Forecast a Better Season?

Major League Baseball players make many millions of dollars because of the feats their bodies perform. When those bodies get bigger or harder or stronger, it might make them even more valuable. It might make them better at baseball.

In a world where employees were perfect, it wouldn’t be newsworthy when a professional athlete went to work in great shape. In that world, a player who bragged to reporters about his narrower waist and wider muscles would be met by blank stares. Great, guess you get to keep your roster spot, someone might say to end the awkward silence.

But we don’t live in that world. Instead, we live in a world where Bartolo Colon gets to bat, so when a baseball player bulks, cuts, spends the offseason at a fitness facility, touches his toes without bending his knees, or discovers what the calorie counts on menus mean, it’s a story. It’s almost impossible to parody the tradition of players reporting to spring training in the proverbial best shape of their lives, because the news itself sounds like satire. In 2011, the Onion-esque site Sports Pickle made thirtysomething backup catcher Miguel Olivo the subject of a story about a player arriving in camp in the fourth-best shape of his life. Miguel Olivo! Pretty random, right? Well played, Sports Pickle. Better, though, was when the real Olivo declared himself to be in the best shape of his life a year later,1 as if the Sports Pickle post had inspired him to hit the gym harder.


In retrospect, “Mariners sensation Jesus Montero” seems like the funniest phrase in that story. Or maybe not, now that Montero has gotten into sensational shape.

For almost any joke about the “best shape” phenomenon that one might think to make, there’s a real player who has already supplied the punch line. Pay close enough attention and you’ll find that the genre even has its own recurring characters — BSOHL recidivists, such as Brian McCann, for whom every spring brings a new self-professed peak.

Before the 2008 season, McCann added “noticeable muscle” and took up yoga. Before the 2009 season, he lost 20 pounds, citing soreness toward the end of 2008 and crediting cardio and a healthy diet. Before 2010, he “rearranged” his weight. And before 2011, he again complained of exhaustion at the end of the previous year, again announced significant weight loss, and again claimed to be “making the nutrition side of it a priority,” as if he hadn’t had the same epiphany about being “conscious of what I was putting in my body” two years earlier. Russell Martin, meanwhile, had a streak of five straight springs from 2008 to 2012 in which he reported reaching new heights of fitness, then took 2013 off before duping yet another writer into praising his physique last September.2 And then there’s the subset of players who ride what HardballTalk blogger Craig Calcaterra — who’s owned the BSOHL beat for the past few years — calls the “BSOHL carousel,” bouncing back and forth between bulking up to add strength and durability and slimming down to focus on flexibility and conditioning, until one of those regimens happens to coincide with a successful season.


Don’t be too hard on McCann and Martin: Maybe they have to tell themselves they’re stronger than ever in order to face the prospect of six more months of punishment behind the plate.

Decades after “best shape” became a cliché, beat writers still scour the clubhouse for signs of weight loss, muscle gain, or reduced body-fat percentage, like middle schoolers sizing up growth spurts after summer vacation. They do this partly because spring training is a series of slow news days, because it proves they have a press pass, and because Been working out this winter? beats Slaughtered many majestic beasts lately? as a clubhouse icebreaker. Primarily, though, they do it because — much as we mock the idea — it might actually matter. Major league players make many millions of dollars because of the feats their bodies perform. When those bodies get bigger or harder or stronger, it might make them even more valuable. It might make them better at baseball.

For every Carlos Gomez, who broke out after sounding the best-shape siren in 2013, there’s a Prince Fielder, who broke down after making the same proclamation last season. If an effect exists, though, it should show up in the stats, given a large enough sample.3 Four years ago, Rob Pettapiece4 searched for a “best shape” effect in a small-scale study at Baseball Prospectus. Here, I’ll be updating his work using a slightly different methodology and a larger sample that includes pitchers as well as position players.


In certain small samples, it definitely doesn’t.


Who’s now an analyst for the Leafs.

Relying on Pettapiece’s player list, Calcaterra’s vigilance, and my own Googling, I assembled a list of 199 player-seasons (143 by position players, 56 by pitchers) by 164 unique players since the spring of 2006 who met my subjective “best shape” standards. If a player pronounced himself in the BSOHL, he was guaranteed a spot in the study; if the magic words weren’t spoken, I weighed his qualifications based on the reported change in his weight, composition, and lifestyle, and any testimonials from his teammates and coaches. “Best shape” status is relative — even David Wells was once a best-shaper — so the key is to compare players to their pudgier selves. Once I had my players, I compared their actual statistics in their “best-shape” seasons to the stats that the current version of PECOTA, Baseball Prospectus’s projection system, would have forecast for them entering those years, knowing only how they had performed to that point — a retro-projection, essentially, or retrojection. If the players outperformed those retrojections — as one would expect them to, if their increased dedication to fitness had really made them new men — then the difference between their projected stats and actual performance would reveal a best-shape benefit.

For hitters, I focused on average plate appearances and True Average, BP’s all-encompassing offensive rate stat, which (like batting average, in most seasons) is scaled to a .260 league average. The BSOHL position players averaged 29.8 years old and were projected to be above-average hitters, which makes sense: Players generally don’t get press for being in better shape unless reporters have been around them before, and no one cares how hard the weak-hitting fifth outfielder worked over the winter.

The initial results don’t pop off the page:5

BSOHL Hitters
True Average PA Per Player
Projected .275 459
Actual .275 469


I tried multiple methods of weighting the True Averages by playing time, both of which produced similar results. The values in the second column in the table below are weighted by the minimum of each player’s projected and actual plate appearances — in other words, both the projected and actual True Averages were weighted by the smaller of the two PA figures, so that the hitters who exceeded their projected playing time wouldn’t be weighted more heavily in the “Actual” group.

What we want to know is the difference between the projected and actual True Averages — and it looks like there isn’t one. On a per-plate-appearance basis, the BSOHL players hit exactly as well as PECOTA projected them to, although they did get slightly more playing time than projected (more on that in a moment).

PECOTA’s retrojections serve as a quasi-control group for the BSOHL hitters, but it’s possible that PECOTA systematically under- or over-projects the older, better hitters who tend to be in the BSOHL group, which could skew the results. To rule out that possibility, we can create a real control group of players who didn’t declare themselves to be in the best shape of their lives, and examine the difference between their projected and actual performances. The control players were picked programmatically by matching each BSOHL hitter to the closest player comp in the same season, based on proximity in age, position, and projected stats. The control comp for 27-year-old Brewers backup catcher Martin Maldonado (a best-shaper in 2014), for instance, was 27-year-old Cardinals backup catcher Tony Cruz. Here are the control-group results:

Control-Group Hitters
True Average PA Per Player
Projected .274 431
Actual .276 411

Again, we see essentially no difference in True Average; if anything, the non-BSOHL hitters hit slightly better than the best-shapers, relative to their projections. This time, though, we see a playing-time difference in the other direction: The non-BSOHL hitters played less than they were projected to, even though we excluded anyone who didn’t play in the majors from the player-comp pool.

The BSOHL players in the first table exceeded their projected playing time by 10 plate appearances each, on average. The control players in the second table fell 20 plate appearances short of their projected totals, on average. That’s a difference of 30 plate appearances per player, which comes close to statistical significance.6 One obvious explanation is that the best-shapers, buoyed by their newfound fitness, tend to suffer fewer injuries than the non-best-shapers. We can test that theory by running the BSOHL and control-group players through BP author Rob Arthur’s position-player injury prediction model, which uses data from the BP injury database to predict days lost to injury based on injury history and age. The following table lists the average days missed per player in the previous season, the predicted days missed per player in the best-shape season, and the actual days missed per player in the best-shape season, for both groups.

Group Days Missed in Previous Season Predicted Days Missed Actual Days Missed
BSOHL 27 24 23
Control 20 23 25



It’s not surprising that the BSOHL players tend to be coming off more injury-plagued seasons than the non-BSOHL players; the rehab process can improve a player’s fitness level, and players who are coming off injury years have more incentive to tell reporters that they’re in peak condition. (The 2015 crop of best-shapers includes several players who missed significant time last year: Mark Teixeira, Maicer Izturis, Avisail Garcia, Bobby Parnell, Carlos Correa.) The third and fourth columns, though, don’t show a dramatic difference: The best-shapers, in their BSOHL seasons, miss one less day than predicted, while the non-BSOHLers miss two more days than predicted. Maybe there’s a hint of an actual effect there, but the apparent tendency for BSOHL players to make more plate appearances could have other causes. Maybe newly fit players need fewer days off, or at least manage to convince their coaches that they’re not in need of rest. Or maybe managers award extra playing time as a reward for hard offseason work.

I suspected that best-shape status might benefit older players more so than young players who are more likely to be in excellent shape to begin with. In fact, the opposite is the case: When I limited the sample to 73 BSOHL seasons by players age 29 or older, most of the playing-time advantage evaporated, and the actual True Averages fell slightly below their projections. Intriguingly, though, the playing-time boost seems to get stronger — on the order of 60 PA per player — if we allow players of all ages but remove McCann, Martin, and the other boys who cried BSOHL, limiting the sample to hitters who’ve only claimed to be in their best shape once. Even in their case, though, there’s no evidence of improved performance in those plate appearances.

One-Time Hitters
BSOHL True Average PA
Projected .272 432
Actual .274 457
Control True Average PA
Projected .271 414
Actual .272 379

A quick note about defense, which seems like a skill that would improve when a player is lighter on his feet: PECOTA retrojections don’t include fielding, so we can’t compare directly, but I did dig through dusty PECOTA spreadsheets from 2006 to 2014 to look up how the same players were projected to do on defense at the time. Contemporary projections forecasted the BSOHLers to be 114 runs below average as a group, and the actual results were much rosier: 31 runs below average. Adjusted for playing time, that’s a roughly half-run raise per player. That’s far from definitive, but it does support the idea that BSOHLers become better defenders. Baserunning results don’t show the same trend.

Lastly, let’s take a look at pitchers. This time, the sample is smaller — 56 player-seasons, or a little less than 6,500 innings — but the results are more encouraging:

BSOHL Pitchers
ERA True Average Allowed IP Per Player
Projected 4.02 .262 113
Actual 3.90 .258 114

BSOHL pitchers beat their projections by a little more than a tenth of a run in ERA and four points of True Average allowed. And the control group of pitchers from the same seasons?7

Control-Group Pitchers
ERA True Average Allowed IP Per Player
Projected 3.80 .261 118
Actual 4.00 .266 116


Determined, this time, by proximity in age, projected-stat similarity, and the ratio of games started to games pitched.

The non-BSOHL comp pitchers underperformed by two-tenths of a run and five points of TAv. Advantage: best-shapers. We don’t have pitcher injury predictions, but we do know that the BSOHL pitchers were coming off seasons in which they missed, on average, 35 days to injury, while the non-BSOHL pitchers were coming off seasons in which they missed only 22. Injuries are a bigger threat to pitchers than they are to position players, so it’s possible that knowing a pitcher is in the best shape of his life — and therefore, one would assume, less likely to be impaired by an injury — tells us more than it does when a position player says the same thing.

So what can we conclude? Being a best-shaper isn’t a magic bullet, but it’s not necessarily as meaningless as the reflexive jokes suggest. BSOHL position players don’t hit any better than expected, but they do tend to beat their playing-time projections and potentially play better on defense. BSOHL pitchers, meanwhile, seem to enjoy a genuine advantage. By the standards of most stories out of spring training, that makes being in better shape big news.

Thanks to Rob McQuown and Rob Arthur of Baseball Prospectus for research assistance.