How can you not be romantic about baseball?

Observe Pablo Sandoval and I can see how not.

It’s a polarising game but one where satisfaction can be found through multiple facets – including baseball statistics, formally known as sabermetrics.

Nevertheless, how does this pertain to nutrition?

Sporting performance and recovery may be enhanced by well-chosen nutrition strategies. Evidence reinforces this understanding. For instance, six of eight studies included in a systematic review reported that carbohydrates, consumed in the form of a 6-8% solution of glucose, sucrose or maltodextrin, enhanced at least one aspect of skilled performance over the duration of exercise (Thomas et al. 2016).

Following a quick review of the evidence, baseball performance is seldom referenced in scientific literature. With this, I wondered, is it possible to ‘anecdotally’ analyse the effect of diet on performance using available baseball statistics?

Originally defined by Bill James, sabermetrics is the search for objective knowledge about baseball; the empirical analysis of baseball. The subject, much like nutrition, is constantly evolving. However, a plethora of stats remain for performance analysis.

Obviously, the sincere inability to confirm reported dietary changes with athletes limits the credibility of this post – nonetheless, the following is an interesting experiment and according to online searches, explicit dietary alterations have been well documented for Mark Teixeira (NYY, 1B) and Collin McHugh (HOU, SP). Both of whom have adopted a gluten-free diet (GFD).

Whether either of these players have made an adjustment due to a diagnosis of coeliac disease, I, again, cannot confirm. Regardless, below are some stats pre- and post-adjustment for each player:

Mark Teixeira
2015 111 462 31 57 79 2 12.8 18.4 .293 .246 .255 .357
2014 123 508 22 56 62 1 11.4 21.5 .182 .233 .216 .313

Table 1: 2014, Pre-GFD; 2015, Post-GFD.

Collin McHugh
2014 25 154.2 25.4 6.6 18.7 0.76 .259 2.74 3.11 3.14 1.02 11.0
2013 7 26.0 8.8 4.0 4.8 2.08 .379 10.04 5.78 4.86 1.92 8.5

Table 2: 2013, Pre-GFD; 2014, Post-GFD.

Teixeira presents the most credible sample size for this exercise and, when referencing these fundamentally basic stats, exhibits an improvement in performance across the board. Specific for offensive statistics that stabilise soonest, Teixeira, in the short-term, bettered his Strikeout Rate (K%), Walk Rate (BB%), Homerun (HR) Rate, On-Base Percentage (OBP) and Isolated Power (ISO) in the year following his adoption of the GFD. With such ubiquitous improvement, Teixiera provided an objectively greater total offensive value, presenting approximately a 42% increase in Weighted Runs Created Plus (WRC+).

Unlike Teixeira, McHugh provides a slightly more complicated image as a result of lacking historical context. In 2013, McHugh pitched just 26.0 innings at the MLB level, whereas in 2014 he accumulated 154.2 innings of work.

In those 26.0 innings, McHugh faced a total of 125 batters. Based on stabilisation points for pitching statistics, this restricts any objective analysis to just changes in strikeout rate (K%); which, evidently, observed a drastic improvement. Interestingly, McHugh was never considered an exceptional baseball prospect. However, his 2014 performance was incredible. Among pitchers with 150 or more innings, McHugh ranked 15th in Earned Run Average (ERA), 6th in Walks Plus Hits per Inning Pitched (WHIP), 9th in Strikeout Rate, and had the 14th best Swing Strike Percentage (SwStr%).

Nevertheless, a cautionary tale was to be found within McHugh’s stats. A Batting Average on Balls in Play (BABIP) below a league average .300 fundamentally suggested luck, whereas a Fielding Independent Pitching (FIP) and Skill-Interactive ERA (SIERA) both greater than ERA suggested the potential for negative regression – which McHugh has experienced since 2014.

Regardless, despite the obvious short-term improvements in statistical performance, these cannot be exclusively attributed to the documented dietary change. Yet, if, hypothetically, diet were to be considered a significant influencer, does an explanation for any relationship exist?

Speculatively, a relationship between diet and performance may result from dietary effects on human gut microbiome diversity. The human intestinal tract harbours a collection of beneficial bacteria that perform an array of functions and increasing evidence indicates that changes in the composition of the human gut microbiota affect host metabolism and are associated with a variety of disease (Shoaie et al. 2015).

Changes in diet have been shown to rapidly affect the composition of the gut microbiota. Furthermore, microbiota-diet interactions impact host physiology through the generation of a number of bioactive metabolites. For example, short-chain fatty acids (SCFAs), which are generated by microbial fermentation of dietary polysaccharides (e.g., starch) in the gut are an important energy source for colonocytes and also function as signalling molecules, modulating intestinal inflammation and metabolism (Shoaie et al. 2015).

In a study pertaining to personalised nutrition, alterations in gut microbiota following personally tailored dietary interventions were evident. While many changes were person-specific, several taxa changed consistently in most participants. For example, Bifidobacterium adolescentis, for which low levels are reported to be associated with greater weight loss, generally decreased following the “good” diet and increased following the “bad” diet (Zeevi et al. 2015).

This susceptibility of the gut microbiota to the influence of the diet is further evident with relation to the GFD. Yet, somewhat peculiarly, analyses of faecal microbiota and dietary intakes have indicated that populations of generally regarded healthy bacteria decreased, while populations of potentially unhealthy bacteria increased parallel to reductions in the intake of polysaccharides after following the GFD. These findings indicate that this dietary therapy may contribute to reducing beneficial bacterial counts and increasing enterobacterial counts, which are microbial features associated with the disease and, therefore, it would not favour completely the normalisation of the gut ecosystem in treated coeliac disease patients (Sanz 2010).

What does any of this mean?

Clearly, a GFD has the potential to alter the microbiome of the gut. However, specific to coeliac disease, it’s physiological impact and thus therapeutic efficacy, from this perspective, is debatable. Regardless, doubtful that either Teixeira or McHugh have been diagnosed with coeliac disease, it’s possible that diet-related changes in the gut microbiome influence cognitive functions. Hence, a potential link to baseball.

Subjectively, cognitive function could be assumed as strongly relevant to baseball performance. Interestingly, in a rodent study, both high fat and high sucrose diets (dietary attributes reminiscent of the current Western diet) were shown to interfere with cognitive flexibility. For example, disregarding the noted alterations in the microbiome, mice on the high sucrose diet were impaired in learning a new platform location, suggesting the mice had deficits in cognitive flexibility (Magnusson et al. 2015). Considering that cognitive flexibility is defined as the human ability to adapt the cognitive processing strategies to face new and unexpected conditions in the environment (Cañas et al. 2003), any influence on this could impact performance.

Thus, while this is an entirely abstract post and honestly provides no nutrition-related justification for the improved statistical performances, there may be some chance (albeit, probably minimal) that even short-term dietary interventions which have been shown to significantly alter the gut microbiota can influence human performance.