Jays From the Couch looks into the numbers and argues that the Toronto Blue Jays are not as bad as their poor start would lead you to believe

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Throughout the Blue Jays’ (ongoing) rough start to the season, I have continually asked myself, “Is this team really as bad as they look?” Two weeks ago, we were fairly confident that this team would be in contention for a wild card spot and maybe, if things worked out just right, an AL East title. Things have not worked out that way just yet. With Josh Donaldson, Aaron Sanchez, J.P. Howell and, potentially, J.A. Happ on the disabled list, the situation might get even more dire.

A 2-10 run (1-9 in their first ten games) is rough at any time of year, but gives fans that extra punch in the gut when it happens right at the start of a season. The untimely losing streak led one particular “fun fact” to echo throughout the Twitterverse: no team that started the season 1-9 has ever made the playoffs. That’s true, but here are more fun facts.

Correlation does not necessarily imply causation. Seven teams have started the season with a 1-9 record since 2002. None made the playoffs. This is mainly because they were bad teams, not because of their bad start. Six of those teams ended up with 93 losses or more. Only one of those teams even sniffed a .500 record in the last 152 games; Houston finished with a 76-86 record in 2010, going 75-77 after their poor start. But even they were far from World Series hopefuls, ranking 23rd in preseason WS odds that year (80 to 1).

Can the Jays be the first 1-9 team to reach the playoffs? Maybe. It’s not impossible, but that depends on many other, unpredictable factors (namely the performances of other teams). So here’s a better, simpler question: Are the 2017 Blue Jays “worse” than we thought? The TL;DR answer: No, across many peripheral statistics, they’re on par with 2015/16 and their main issue genuinely seems to be bad luck on an almost incomprehensible level. I know, that’s quite the statement. To some extent, I’m having trouble believing it myself; but it really does seem that some key peripheral statistics have not changed much from last year. Which means that, going forward, we should expect the team to perform close to the level we expected them to (one obvious caveat being that long-term injuries to a few key players would decrease their expected performance level).

At this point in the season, the sample size is so small that top-line numbers (like batting average or earned run average) are very “noisy”. Nate Silver’s 2012 book The Signal and the Noise examines the difference between signal and noise. In baseball terms, signal generally refers to a player’s genuine ability. This is fairly consistent over time. Noise refers to the random variation that occurs from game to game, week to week and year to year. Statistical noise helps explain why a player can be worth 7 wins above replacement (WAR) one year, then follow it up with a 1 WAR season the next. They’re actually a 4 WAR player (signal), but noise drove their single-season performance up and down by 3 WAR.

Let’s think of signal and noise in practice. Last Tuesday, during their home opener, the Jays were losing 4-3 in the seventh inning against the Milwaukee Brewers. They had two outs, with Devon Travis on second and Kendrys Morales on first. Troy Tulowitzki hit a grounder to the left side with an exit velocity of 107.9mph and a 62% hit probability, according to Baseball Savant. That is the signal. The grounder was fielded by the shortstop for an out, ending the inning and preventing the tying run from scoring.

That’s the noise, as is the fact that if Tulo made contact with the ball a few millimetres lower, it might have been an extra base hit. Earlier in the same game, Keon Broxton hit a fly ball with an exit velocity of 96.3mph and a hit probability of 25%. That is the signal. It went over the wall, making it 1-0 for the Brewers in the top of the first. As of Friday night, it was the 317th slowest home run of 2017 by exit velocity, out of 332 in total. That is the noise.

Top-line numbers suggest that the Blue Jays offence and starting rotation, both Top 10 producers in 2016, are measurably worse than last year. This is the noise.

Are Jays hitters and starters actually worse? Here’s a thought experiment: When a hitter goes up to bat, what are the possible outcomes of the plate appearance? They can walk, strikeout or put the ball in play (that includes home runs in this context). A ball in play can be either a groundball, fly ball or line drive. The quality of contact for any of these batted ball types can be either hard, medium or soft, as measured by Baseball Info Solutions (BIS) and reported by Fangraphs.

All told, there are 11 possible outcomes of a plate appearance within this framework. These 11 outcomes are generally the best to focus on when dealing with a small sample size as they become reliable at a much lower sample size than results-based statistics like AVG and OBP). Let’s call them signal outcomes, as they better reflect a player’s genuine ability, particularly at this early stage of the season.

There are also a couple of statistics that are generally viewed as indicators of luck, home run to fly ball ratio (HR/FB%) and batting average on balls in play (BABIP). In general, batters and pitchers don’t really determine whether a fly ball will go out of the park for a home run. Sometimes they do and sometimes they don’t. A team with a higher than normal HR/FB% is usually just benefiting from good luck, while a team with a lower than normal HR/FB% is usually just suffering from bad luck. Since 2002, the league average HR/FB% is 10.6%, with 398 of 450 teams producing a HR/FB% between 7.6% and 13.6%. Batters and pitchers also generally can’t exert much control over whether balls in play will turn into hits or outs. Since 2002, the league-average BABIP is .298, with 425 of 450 teams producing a BABIP between .278 and .318.

One way to determine if Jays hitters and starters are markedly worse is to compare their collective signal outcomes from 2015 and 2016 to their corresponding levels so far in 2017. In general, walks, hard groundballs, hard fly balls and line drives (of any contact quality) are “good” signal outcomes. Strikeouts, soft- or medium-hit ground balls and soft- or medium-hit fly balls are “bad” signal outcomes.

[On a technical note, I chose to examine team-level data because of the sample size. Any small sample issues with my analysis would be magnified significantly with player-level data. Finally, a shout out is due to Fangraphs for the great splits tool that they’ve developed, not to mention all of the other great data on their site. While users can easily access batted ball data and quality of contact data separately, the splits tool allowed me to examine their interactions (e.g. hard fly balls, soft line drives, etc.).]

It’s fair to say that a team’s hitting has gotten fundamentally worse when they produce:

more medium- or soft-hit groundballs

more medium- or soft-hit fly balls

more strikeouts

fewer hard-hit groundballs or fly balls

fewer line drives

fewer walks

Moreover, if a team’s hitters produce a BABIP or a HR/FB% that is greater or equal to previous seasons, it’s unlikely that they are merely suffering from bad luck.

When comparing the Jays’ hitting from 2017 to the glory years of 2015 and 2016, it seems that not a whole lot has changed for the worse. They are producing fewer soft/medium groundballs and a similar proportion of line drives, walks and strikeouts. The proportion of medium- or soft-hit fly balls is up, but only slightly. Hard-hit fly balls are down 3%, but hard-hit groundballs are up by 3%. This trade-off is not quite a wash, as hard-hit fly balls are more valuable than hard-hit grounders. That said, remember Tulo. With a team of upper-cut hitters like the Blue Jays, the difference between a hard-hit grounder and a hard-hit fly ball is often just a few millimetres.

It seems that the main differences between 2017 and 2015-16 are the measures of luck. The mashers of 2015 and 2016 maintained an extraordinary HR/FB% around 15% (Top 5 both seasons), while a similar group of mashers is currently hovering around 6% (28th in MLB). Age and roster turnover might cause a decrease in the Jays’ HR/FB% this year, but a team this powerful should still end up with at least an average HR/FB% (10.6%). Jays hitters also own the 28th-highest BABIP (.253), much worse than the slightly below-average BABIP they produced over the last two seasons.

Sure, it’d be awesome if the Jays had started the season with a Top 10 offence. But what exactly should they have done differently? I’m open to suggestions, but “hit more gooder” doesn’t count as a valid answer. It genuinely seems that if the team maintains their current approach at the plate, their offensive performance will eventually look a lot more like 2015-16.

Shifting focus, a team’s starting pitching has likely gotten worse when they allow:

fewer medium- or soft-hit groundballs

fewer medium- or soft-hit fly balls

fewer strikeouts

more hard-hit groundballs or fly balls

more line drives

more walks

If a team’s pitchers allow a BABIP or a HR/FB% that is less than or equal to previous seasons, it’s unlikely that bad luck is the problem.

Compared to 2016, the starting rotation has produced a greater proportion of strikeouts, while allowing a lower proportion of walks, the two things pitchers generally have the most control over. They’ve also generated more medium-hit fly balls, though soft-hit fly balls are down from last year. On a less positive note, the group has allowed a smaller proportion of soft and medium groundballs. They have, however, played four of ten games (statistics are only through Friday night’s game) against the Orioles, a team that hits relatively few groundballs (27th highest GB% in 2016, 26th highest in 2017) or soft/medium-hit balls (20th highest Soft + Medium % in 2016, 18th highest in 2017).

Blue Jays starters have also seen a small but notable uptick in line drives, hard groundballs and hard fly balls allowed. Again, this might just be a result of their opposition to this point. Baltimore, Tampa Bay and Milwaukee weren’t known as line drive hitters in 2016, but are all in the Top 12 for LD% in 2017. They are all known as hard-hitting teams, sitting in the Top 13 for Hard% in both 2016 and 2017. While I hesitate to use 2017 team statistics to rate the Jays’ opponents so far (the chicken or the egg problem), they appear to have generated line drives and hard contact both against the Jays and against the other teams they’ve faced.

Like our hitters, our starters have also been bitten by the bad luck bug. This season, Blue Jays starters have had a bit of a home run problem. Thus far, they have allowed nearly two home runs per nine innings (1.88 HR/9), 5th highest in MLB. Last year, our starting rotation allowed only 1.08 HR/9 (23rd highest). With a relatively stable fly ball rate, the main culprit seems to be a HR/FB% (21.4%, 3rd highest) that is nearly twice as large as 2016 (12.7%, 21st highest) and more than twice as large as the MLB average (10.6%).

After posting extremely low BABIPs in 2015 and 2016 (2nd lowest in MLB both years), the starters have seen this luck measure rise towards the league average in 2017 (18th lowest). It’s a bit hard to say at this point whether the starters have been unlucky this season or if they were lucky in recent seasons and are now just regressing to the average.

Let’s recap:

Jays hitters are essentially producing the same batted ball-contact quality percentages, strikeout rates and walk rates as in 2015 and 2016

Jays hitters are suffering from a lot of bad luck, with a much lower HR/FB% and BABIP compared to 2015 and 2016

Jays starters have improved upon their 2016 performance in some ways (more strikeouts and medium-hit fly balls, fewer walks)

In other ways (fewer soft-hit fly balls, fewer medium- or soft-hit groundballs, more hard groundballs, more hard fly balls and more line drives), Jays starters have taken a step back

Jays starters have also been unlucky (higher BABIP and much higher HR/FB% than 2016)

Jays relievers are doing pretty much what we expected them to (league average)

When examining a team that started 1-9, there will definitely be room for fundamental improvements. Maybe a “better” approach at the plate will see the hard-hit fly ball rate return to past heights. Maybe adjustments by the pitching staff and Russell Martin will lead to more weak contact and less hard contact. Nevertheless, if you read this article unaware that the Jays started the season with a 1-9 record, I doubt you’d even know there was much of a problem. It seems reasonable to say that if these ten games (and performances) were replayed 1000 times, the Jays would tend to have a 5-5 record, based purely on how similar their peripherals have been to recent seasons (where they won an average of 5.6 games out of every 10 played).

I will admit that, with each disappointing game, bad luck is a tougher and tougher explanation to swallow. At the end of the day though, random variation is baseball, so let’s settle in for a nice, long 162 game season and see what happens. If my analysis is right, we should get our rally caps ready.

*Featured Image Credit: C Stem- JFtC

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