Examining Baseball’s Most Extreme Environment
“The Coors Effect.”
These three words evoke a strong reaction from most people and are impossible to ignore when discussing the offensive production of a Rockies player. Ask anyone who was around for the Rockies of the ‘90s and they will tell horror stories of games with final scores of 16-14. Ask anyone at FanGraphs and they will laugh and point at the Rockies’ 2015 Park Factor of 118. Heck, ask Dan Haren and see what he has to say:
I would count out the days about a month in advance to see if I was gonna pitch in Coors field.
— dan haren (@ithrow88) January 4, 2016
Suffice it to say that Coors is a hitter’s park. Nobody will argue that. But there have been murmurs recently about another effect of playing 81 games at altitude, an effect that actually decreases offensive production. These murmurs have evolved into a full-blown theory, which has been labeled the “Coors Hangover.”
This theory supposes that a hitter gets used to seeing pitches move (or, more accurately, not move) a certain way while in Denver. When they go on the road, the pitches suddenly have drastically different movement, making it difficult to adjust and find success at lower elevations. In other words, Coors not only boosts offensive numbers at home, it actively suppresses offensive numbers on the road, which can take relatively large home/road splits for Rockies players and make them absolutely obscene.
The concept seems believable, but thus far we have no conclusive evidence of its merit. FanGraphs’ Jeff Sullivan recently tested this theory, as did Matt Gross from Purple Row. Although neither article revealed anything promising, Jeff is still a believer, as he recently shared his personal opinion that the Coors Hangover might simply last longer than any 10-day road trip. With this is mind, I decided to approach the problem by examining the park factors themselves.
If you haven’t read the article about how FanGraphs calculates its park factors, I highly recommend you do so before continuing. The basic approach detailed in that article is the same approach that I use here. As a quick example, the park factor for the Rockies is calculated by taking the number of runs scored in Rockies games at Coors (both by the Rockies and the opposing team) and comparing that to the number of runs scored in Rockies games away from Coors. Add in some regression and a few other tricks, and we have our final park factors.
This method makes a number of assumptions, most of which are perfectly reasonable, but I was interested in taking a closer look at one critical assumption. By combining the runs scored by the Rockies with the runs scored by their opponents, we are assuming that any park effect is having an equal (or at least, an indistinguishable) impact on both teams. This seems like an obvious assumption, but it becomes invalid when the Rockies play on the road. According to the Coors Hangover, Rockies hitters experience a lingering negative park effect after leaving Coors which the opposing team is not experiencing.
In other words, we expect a gap to exist between a hitter’s performance at Coors and his performance at an average park. If the Coors Hangover is true, this gap would be larger for Rockies hitters than anyone else.
Let’s start by taking a look at the park factors we have now. The following tables only contain data from NL teams for simplicity sake.
Park Factors, 5-year Regressed (2011-2015) | |||
Team | Total Runs (team + opponent) | Park Factor | |
Home | Away | ||
Rockies | 4572 | 3205 | 1.18 |
D-backs | 3657 | 3328 | 1.04 |
Brewers | 3588 | 3306 | 1.04 |
Reds | 3385 | 3215 | 1.02 |
Phillies | 3365 | 3341 | 1.00 |
Nationals | 3240 | 3213 | 1.00 |
Cubs | 3346 | 3345 | 1.00 |
Marlins | 3200 | 3229 | 1.00 |
Braves | 3086 | 3199 | 0.99 |
Cardinals | 3243 | 3397 | 0.98 |
Pirates | 3070 | 3394 | 0.96 |
Dodgers | 2995 | 3323 | 0.96 |
Mets | 3109 | 3556 | 0.95 |
Padres | 2936 | 3440 | 0.94 |
Giants | 2900 | 3537 | 0.92 |
No surprises. Teams score a ton of runs at Coors and hardly ever score at AT&T Park in San Francisco. Now let’s split up those middle columns to get a closer look at who is scoring these runs.
Runs Scored, 2011-2015 | ||||
Team | Home Stats | Away Stats | ||
Team | Opponent | Team | Opponent | |
Rockies | 2308 | 2264 | 1383 | 1822 |
D-backs | 1844 | 1813 | 1641 | 1687 |
Brewers | 1823 | 1765 | 1619 | 1687 |
Reds | 1731 | 1654 | 1606 | 1609 |
Phillies | 1676 | 1689 | 1576 | 1765 |
Nationals | 1749 | 1491 | 1651 | 1562 |
Cubs | 1625 | 1721 | 1547 | 1798 |
Marlins | 1541 | 1659 | 1464 | 1765 |
Braves | 1606 | 1480 | 1569 | 1630 |
Cardinals | 1779 | 1464 | 1797 | 1600 |
Pirates | 1586 | 1484 | 1688 | 1706 |
Padres | 1443 | 1493 | 1604 | 1836 |
Dodgers | 1557 | 1438 | 1758 | 1565 |
Giants | 1481 | 1419 | 1797 | 1740 |
Mets | 1482 | 1627 | 1817 | 1739 |
These are the two pieces of run differential — runs scored and runs allowed — and we generally see agreement between the home and away stats. If a team out-scores their opponents at home, they can be expected to do the same on the road. Good teams are better than bad teams, regardless of where they play. Although, if you subtract a team’s run differential on the road from their run differential at home, the difference will actually be around 100 runs due to home-field advantage. Doing this for all 30 teams yields a mean difference of 83 runs with a standard deviation of 122.
Where do the Rockies fall in this data set? Not only have they scored over 400 more runs at home than the next-best NL team — they have also scored almost 200 runs less on the road than the next-worst NL team. Comparing their home and road run differentials, we see a difference of 483 runs (+44 at home, -439 on the road), or 3.3 standard deviations above the mean. To put it plainly: that’s massive. This is a discrepancy in run differentials that cannot be explained by simple home-field advantage.
Furthermore, I followed the same process of calculating park factors for each team explained above, but I split up the data to calculate a park factor once using the runs scored by each team (tPF), and again using the runs scored by each team’s opponents (oPF). Generally, these new park factors are closely aligned with the park factors from before…except for, of course, the Rockies.
Alternate Park Factors, 5-year Regressed (2011-2015) | ||
Team | tPF (Team Park Factor) | oPF (Opponent Park Factor) |
Rockies | 1.27 | 1.10 |
D-backs | 1.05 | 1.03 |
Brewers | 1.05 | 1.02 |
Reds | 1.03 | 1.01 |
Phillies | 1.03 | 0.98 |
Nationals | 1.02 | 0.98 |
Cubs | 1.02 | 0.98 |
Marlins | 1.02 | 0.97 |
Braves | 1.01 | 0.96 |
Cardinals | 1.00 | 0.96 |
Pirates | 0.97 | 0.94 |
Padres | 0.96 | 0.92 |
Dodgers | 0.95 | 0.97 |
Giants | 0.93 | 0.92 |
Mets | 0.92 | 0.97 |
On average, a team’s tPF is about two points higher than its oPF — again, this can be attributed to home-field advantage. The Rockies, however, are in an entirely different zip code with a discrepancy of 17 points. We aren’t talking about home-field advantage anymore. We are talking about something deeper, something that should make us stop and think before averaging the two values to get a park factor that we apply to the most important offensive statistics.
We have no reason to believe that any team should have a 17-point difference between their tPF and oPF; the fact that the Rockies are in this situation either means that they are enjoying hidden advantages at home, or they are suffering hidden disadvantages on the road. To date, we don’t have a theory supporting the former, but we do have one supporting the latter. This is the Coors Hangover.
Does this mean that the Rockies’ Park Factor should actually be their oPF of 110? Should it be some weighted average of different values? I don’t know. But I do know these numbers can’t be ignored. Something is going on here, and we need to talk about it.