Jeff Q’s Bi-Weekly Blue Jays Statstravaganza: Inaugural Edition

 

Jays From the Couch resident statsman, Jeff Q breaks down the Toronto Blue Jays first two weeks of the 2018 season

 

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What a time to be a Blue Jays fan. We are halfway through April and the team already has nine wins under their belt. That’s a big deal. Over the last four seasons, fourteen games in, the Blue Jays had accumulated win totals of eight (2014), seven (2015 and 2016) and three (2017).

 

As the season wears on, the abundance of baseball stats helps us better understand how well the team is truly playing relative to the rest of the league. I thought it might be useful to run a regular segment here on Jays from the Couch that updates fans on where the Jays stand in terms of all of those stats. My hope is that this series helps the analytics-types get their fix of Blue Jays fancy stats, while giving the less stat-minded among us an easy-to-understand breakdown of how the team is doing across a number of dimensions.

 

My main intention is to provide both surface-level and underlying stats. That will help us track how the team is doing, as well as how sustainable the performance is. That way, when the team is suffering from bad luck, we might avoid feeling too down on them. Similarly, when the team is benefiting from good luck, we might avoid getting too far ahead ourselves and experiencing a rough come down when reality sets in (unless, of course, these emotional swings are what make you love being a sports fan).

 

The stats will come mainly from Fangraphs and Statcast. Readers who are still learning about some of the more advanced stats can find informative glossaries in the preceding links, with detailed explanations for each of the stats I will use in this series.

 

Obviously, this first edition of Jeff Q’s Bi-Weekly Blue Jays Stats Extravaganza comes with a small sample size warning. That said, the underlying stats will be particularly useful in providing context to the normal, results-based stats this time around, as they have more value in these small sample size situations. [Includes data up to and including Friday night’s games.]

 

 

Let’s start with a look at how the bats are doing. Very good. The bats are doing very, very good. They are driving the team to an average of 5.14 runs per game, sixth-best in the majors. While their overall hitting production isn’t quite as dominant (a just above-average wRC+), the team is producing very dangerous contact (a top five xwOBA). This strong performance seems pretty legit, at this point.

 

The team’s strong offence is not quite as evident in their triple slash statistics. The Jays are running a below-average AVG and OBP, something fans have gotten used to over the last couple of years. On the other hand, they’re running a top ten SLG—this imbalance between getting on base and hitting for power has become a standard trait of the post-2015 Blue Jays teams.

 

The Jays’ plate discipline performance this season also seems familiar—a few more walks than most, but a few more strikeouts than most, too. The combination results in a below-average walk-to-strikeout ratio. That said, the team is oozing with power this season (second-best ISO). Importantly, that power isn’t coming from fly balls that barely scrape past the Dome’s modest right and left field corners. It’s coming from good, old-fashioned barrels—the team is running the league’s second-best barrel rate, implying that we should expect to see the Jays near the top of the ISO charts throughout the season.

 

While the classic luck indicators offer contrasting stories, it seems likely that only one is “telling the truth”. The Jays’ super-low BABIP implies that the team has experienced bad batted ball luck this season. The general view is that a team’s BABIP should end up around the league average. When a team has a BABIP that’s well below-average (like the Jays), it might mean that they are due for some more base hits going forward.

 

Does this logic apply to the Jays? Well, on the one hand, the Jays produced a .276 BABIP last season, which suggests that Jays batters may be prone to low BABIPs, in which case there would be no reason to expect things to change going forward. On the other hand, the team has a huge gap between its xBA and BA on batted balls, with the Jays owning the league’s best xBA on batted balls mark, but a below-average BA on batted balls. [Note: This Statcast version of BABIP includes HR in the calculation, but doesn’t include SF. I try to differentiate the two by calling this one BA on batted balls, since homers are batted balls, but aren’t considered “in play”.]

 

The xBA-BA gap implies that the contact that Jays batters have generated so far justified many more hits than they actually got. Moreover, this big gap wasn’t present in 2017. Last season, the team finished with an xBA on batted balls (.321, 25th in the majors) that was just as bad as their BA on batted balls (.316, 28th best). This suggests that the Jays should probably expect to produce a higher BABIP going forward this season, which would then support a higher wRC+.

 

The other classic luck indicator, the home run-to-fly ball ratio, is saying quite the opposite. With the league’s fourth-highest mark, one might think that the team has been getting lucky, in terms of homers (this stat works a lot like BABIP, as teams are generally expected to end up around the league-average). However, if we dig deeper, we can see that a high HR/FB% is sustainable for the Blue Jays. The simplest reason is history—the Jays have produced a HR/FB% between 14.3% and 15% each of the last three seasons.

 

The better reason for the sustainability of their HR/FB% is another gap, this time between the team’s xwOBA and wOBA on batted balls. Jays batters are absolutely crushing the ball this season (second-best xwOBA on batted balls), but haven’t been fully rewarded for it (an only above-average wOBA on batted balls). The team was in a similar position last season, but the gap was much smaller then—their full-season xwOBA on batted balls was .373, compared to a wOBA on batted balls of .357.

 

While I don’t think the boys can maintain their .440 xwOBA on batted balls—the best mark of the Statcast era is .403, set by Oakland last season—I do think that their .373 wOBA on batted balls could definitely increase with a bit more luck—a .400 wOBA on batted balls is certainly doable.

 

So, in a nutshell, the Jays offence has been very productive (high runs per game), driven by a sustainably strong performance on batted balls and a decent number of walks. The strikeouts are an issue, but not as long as the bats keep mashing.

 

 

The Jays’ front office caught some heat last season for implying that they wanted to get faster, but not backing that up with any clear moves for fast players. The result was the league’s second-worst number of baserunning runs (-15.8 BsR, or roughly 16 fewer runs via good baserunning than the average team). This off-season was a completely different story. In particular, the team’s two outfield additions, Curtis Granderson and Randal Grichuk, have been consistently above-average base runners throughout their careers. The team’s two infield additions, Aledmys Diaz and Yangervis Solarte, are below-average runners, but they’re replacing guys (Ryan Goins, Darwin Barney and, for now, Troy Tulowitzki) who were below-average runners themselves last year.

 

So far, the effects have been quite positive—overall, the Jays have been an average baserunning team. While the team has struggled to steal bases (wSB), they have done a couple of other things well, effectively taking extra bases, while avoiding unnecessary outs (UBR) and double plays (wGDP). In contrast, the 2017 Blue Jays were in the bottom five in all three of these subcategories. Progress.

 

 

The team’s starting pitching hasn’t been quite the success story that it’s offence has been. The rotation’s ERA and FIP are both worse-than-average so far this season. Last year, the injury-plagued rotation produced nearly-identical marks (a 4.57 ERA and a 4.51 FIP), both good for league-average. This season, scoring is down a touch, so those marks aren’t quite as average. The worrisome part is that the rotation’s xwOBA is even worse-than-average, the implication being that the rotation has given up enough good contact to justify its poor ERA and FIP marks.

 

The lone bright spot is a strong strikeout rate, driven by J.A. Happ‘s unreal 32.9% mark. On the other hand, the starters have given up a few too many walks, particularly Marcus Stroman (13.9% walk rate) and Aaron Sanchez (13.4%). The result of an okay strikeout rate and a bad walk rate is a below-average strikeout-to-walk ratio.

 

The long ball has been another issue. Now, the actual rate of homers allowed hasn’t been good, but it also hasn’t been terrible. The real issue lies in the rotation’s barrel rate—it is one of the highest marks in the majors and implies that Blue Jay starters were probably lucky not to see more balls go over the outfield fence this season. I can remember a number of warning track catches this season.

 

The fancy stats do offer some hope. The rotation’s .306 BABIP is much higher than the league-average, which can be an indicator of bad luck. The fact that four-fifths of this rotation produced a .273 BABIP back in 2016 supports the idea that they’ve been unlucky so far. So does their high HR/FB%—we generally assume that pitchers can’t control whether fly balls go for homers, so they should all have roughly league-average home run-to-fly ball ratios. If Blue Jay starters start seeing more grounders end up in gloves (think of Rougned Odor‘s stupid double) and fewer fly balls end up over the fence, their ERA and FIP will improve (yes, I am very much aware of how obvious this sentence is).

 

Unfortunately, the fancy stats offer a bit of pessimism as well. Giving up good contact has been an issue this season, already made clear by the rotation’s high barrel rate. This issue is also evident in the rotation’s xwOBA and xBA on batted balls, with both marks much worse-than-average. Given that those marks are roughly in line with the batted ball results (wOBA and BA)—evident by the similar percentile ranks—it would appear that the starters have gotten what they deserved. Better outcomes will require weaker contact, rather than simply more neutral luck.

 

 

Thus far, we’ve seen that the good offensive results have been supported by good fundamentals, while the poor rotation results have been supported by poor fundamentals. The story is a bit more complicated with the Blue Jays’ bullpen—the results have been good, but some of the fundamentals are worrisome.

 

The bullpen has produced impressive ERA and FIP marks, both comfortably better-than-average. The key to their success certainly bodes well for the rest of the season: striking batters out and limiting walks. The bullpen has much better-than-average strikeout and walk rates, with the result being the league’s best strikeout-to-walk ratio. Danny Barnes has been impeccable in this respect, striking out ten so far, while walking none.

 

The bullpen’s problem has been batted balls. Their overall xwOBA is worse-than-average and lags well behind their ERA and FIP, hinting at this issue. Just like the starters, the bullpen’s poor home run rate probably should be worse, given its terrible barrel rate. When examining batted balls more generally, it’s evident that the batted ball results that Blue Jay relievers have given up (captured by their cumulative wOBA and BA on batted balls) are well-supported by the underlying contact they’ve conceded (captured by their cumulative xwOBA and xBA on batted balls).

 

The traditional luck indicators seem to be sending mixed signals. On the one hand, the bullpen’s BABIP is a bit high, which implies the possibility for some positive regression to the mean. On the other hand, its poor xBA on batted balls suggests that that high BABIP is right where it’s supposed to be. It might indeed fall as the season progresses, but that would be the result of some combination of the ‘pen giving up weaker contact and good luck.

 

The left on base percentage is very worrisome. The rotation had a pretty average LOB%, so I didn’t bother mentioning it then. The bullpen, on the other hand, has an absurdly high and unsustainable LOB%—the highest full-season mark of the last decade was 81.7% by the Braves back in 2013, while the 2017 Blue Jay bullpen could only muster a 72% mark. An inflated LOB% implies that a team’s pitchers have been able to strand runners on base far more often than one would normally expect. As their LOB% invariably regresses towards a normal level, their ERA will increase (by some extent or another).

 

 

The last stop on our tour through the Blue Jays’ season is the defence. This was a weak spot for the team last season, with the Jays ranking poorly by both key defensive metrics—they posted a cumulative -18 DRS (21st in the majors) and -16.7 UZR (25th best). However, the team jettisoned many of their worst performers—Ezequiel Carrera accumulated -14 DRS across the outfield; Jose Bautista was good for -8 DRS in right; Barney (-6 DRS) and Goins (-4 DRS) were subpar as infield backups; Chris Coghlan managed to produce a -4 DRS in very limited action—which offered hope that things might turn around in 2018.

 

So far, it has. The team has put together a combined 7 DRS, good for fourth-best in the majors. By UZR, they haven’t been quite as defensively solid, but there’s a big caveat to add there (which I allude to in asterisk #3 in the table above)—UZR is updated weekly, while DRS is updated daily. So, while the Blue Jays’ DRS is up-to-date, the UZR is behind by a few games. I’ll be sure to time future posts just after UZR is updated. In any case, these defensive stats are much more meaningful with larger sample sizes. So, while things have gone reasonably well so far, it remains to be seen if we can expect to see much better defence going forward this season.

 

If we put all of these observations together, we can leave feeling pretty confident about where the Blue Jays are at this point of the season. The offence appears strong and sustainable, with power (as usual) driving the team forward. Plus, there seem to have been legitimate improvements in the team’s base running and fielding performance.

 

The main weakness, thus far, has been the pitching, an expected strength. While the relievers have produced great results, the starters have generally not. Worse yet, the underlying performances of both groups of pitchers hasn’t been great. Ultimately, given the talent the Blue Jays possess in the rotation and the bullpen, I still feel confident that the pitching staff can meet expectations this season. If they do start pitching as well as they can, alongside a group of position players that has been effective in all respects of the game, the team has the potential to maintain and build upon their excellent start.

 

Expected run differential (xRD/G): An addendum for the stat-heads.

To wrap things up, I wanted to show off an idea I’ve been working on. It’s an attempt at building a metric that brings together a set of advanced stats that each capture a key part of the game of baseball. Together, this set of stats is intended to capture all of the things that a team controls that directly affect the number of runs scored and given up:

  • xwOBA for batters, to capture a team’s production at the plate via strikeouts, walks and contact
  • xwOBA for starting pitchers and relief pitchers, individually, to capture a team’s starting/relief pitching production in terms of strikeouts, walks and contact
  • BsR/600, to capture a team’s overall base running skill (prorated to 600 PA)
  • DRS/150, to capture a team’s overall fielding skill (prorated to 150 games)

 

I started building the metric by regressing each of those statistics against a team’s run differential per game, for all teams by season since 2015 (the Statcast era). Each of the five stats were found to have a statistically significant relationship with a team’s run differential per game (99% confidence level). Overall, the regression resulted in an adjusted-R2 of 0.79, implying that together these five stats effectively explain a team’s run differential per game.

 

Then, I used the regression results to produce an estimated run differential per game (xRD/G) that was based on the five stats. xRD/G seems pretty strongly correlated to a team’s RD/G, with an R2 of 0.80. I’ve highlighted the three Blue Jays teams in this sample to show that this metric produces some pretty intuitive results.

 

 

The 2015 Blue Jays were awesome and own the eighth-best xRD/G of the last three seasons (0.8). They also got a bit of luck, I’m sure most of it in August and September, which propelled them to the third-best RD/G in the sample (1.4). The 2016 Blue Jays were solid (0.5 xRD/G, 21st best) and got about what they deserved (0.6 RD/G, 18th best). Finally, the 2017 Blue Jays, in spite of all of those injuries were still fundamentally a .500 team, with their -0.6 RD/G lagging well behind their 0.0 xRD/G.

 

In the graph below, I plotted teams by their 2018 xRD/G and RD/G. The 2018 Blue Jays have a 0.9 xRD/G, the sixth-best mark of the young season. While they have been over-performing that mark so far this season (1.4 RD/G), visually one can see that this over-performance is relatively modest. The Angels, on the other hand, are over-performing their 1.2 xRD/G by 2.9 runs! The Blue Jays other rival for the second Wild Card spot, the Twins (-0.6 xRD/G), are similarly over-performing thus far (1.3 RD/G).

 

 

Cleveland is the biggest under-performer thus far, with the league’s top xRD/G (2.0) but an average RD/G (0.3).While the Red Sox have been strong (1.1 xRD/G), if over-performing (2.5 RD/G), the Yankees have been very plain so far in 2018 (0.1 xRD/G and 0.0 RD/G). Impressively, the Blue Jays have been equals with the World Champion Astros in terms of xRD/G. In the National League, the West is just absurd, with the Diamondbacks (1.0 xRD/G), Giants (1.0 xRD/G), Padres (0.8 xRD/G) and Dodgers (0.7 xRD/G) leading the way. The over-performing Braves follow in fifth (0.6 xRD/G, 2.3 RD/G).

 

I definitely intend to examine xRD/G further, particularly looking into whether it is a better predictor of future RD/G than RD/G is (as is the case with ERA and FIP). Nevertheless, early indications suggest that xRD/G could be a useful, holistic way to compare a team’s overall on-field results with their underlying performance—in which case it seems fair to say that the Blue Jays have had a strong and sustainable first fourteen games of the 2018 season.

 

For those of you who made it all the way through, feel free to comment about potential stats you’d like me to include in future editions of the Bi-Weekly Statstravaganza.

 

 

 

 

 

*Featured Image Courtesy Of DaveMe Images. Prints Available For Purchase.

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I’m an economics professor in the GTA whose lifelong love for the Jays was reignited by that magical August of 2015 and the amazing moments since.

Jeff Quattrociocchi

I'm an economics professor in the GTA whose lifelong love for the Jays was reignited by that magical August of 2015 and the amazing moments since.