Showing posts with label Run Differential. Show all posts
Showing posts with label Run Differential. Show all posts

05 July 2017

The Orioles and Blowouts

It's not just your imagination: the Orioles are getting blown out a lot. So far this season, the Orioles have had losses of 5 or more runs 15 times, good for 18% of their total games played. More disturbingly, though, is that fact that 11 of those losses have come since June 1, meaning that the Orioles have gotten blown out in a whopping 35% of games in the last month. That, unsurprisingly, coincides with their long and painful skid to a sub-.500 record and increasingly bleak playoff chances.

For context, while 15 total blowout losses isn't horrendous by MLB standards this season, with the average team getting blown out about 12 times, it is the worst mark in the division. Toronto has 13 such losses, Boston 11, Tampa Bay 10, and the Yankees an impressive 6. The O's also have the fourth most blowout losses in the American League, trailing only Minnesota, Oakland, and Kansas City.

The trend looks even worse, however, when you look at recent history for the club itself. The 18% blowout mark would represent the worst such season since 2010, in which the team suffered blowouts in 19% of its games.


Year Blowouts % of Total Games
2017 15 18
2016 21 13
2015 20 12.5
2014 15 11
2013 11 7
2012 23 14
2011 28 17
2010 31 19

While it is not necessarily the case that getting blown out in a high number of games means that a team will miss the playoffs (they all count the same, after all), it is certainly illustrative of the overall problems that the Orioles are facing. The pitching staff has largely imploded over the past month, and even the formerly stalwart offensive attack has been mediocre at best. Blowouts are also surely psychologically difficult to deal with and tax an already depleted bullpen. 

The best teams in baseball, simply, do not get blown out very often. This is perhaps an obvious point, but the Orioles are still hanging onto playoff hopes as we head into the break. Strangely, 4 of the top 5 most blown out teams in the AL are within 2 games of the second wild card and the overall mediocrity in the AL means that it is possible that the O's can survive the recent rash of blowouts. Still, maybe a couple closer games wouldn't be the worst? 

01 May 2017

You Are Not Your April Record...or Run Differential

This tweet was brought to my attention.  Let us not presume what Melewski's intent is here in writing and simply answer the question: what is more important in determining what is likely to occur for the rest of the season?  April Record or April Run Differential.

I took team performance from 2014-2016.  I ran linear modeling for April winning percentage against the rest of the season winning percentage.  Similarly, I ran run differential (as a rate for 20 games) against the rest of season winning percentage.

Utility as a predictor:
April Wins and Losses - r2 = 0.07
April Run Differential - r2 = 0.21

Both are rather poor predictors of future performance, but in practice a difference would be made.  If you were running a population study and saw a correlation of 0.21, then you would likely highlight that as a potential variable that plays a role in whatever you are looking at.  A variable with a 0.07 coefficient is one that will almost always be tossed out as having little to no predictive value.

OK, but what about very successful April teams?  How do the metrics work for them (n=18)?
April Wins and Losses - r2 = 0.03
April Run Differential - r2 = 0.30

This also seems to affirm that April Run Differential is more meaningful, but it may be over or under stating things due to a much smaller sample size.

But what about a team like that Yankees with a plus 40 run differential and a team like the Orioles with a neutral run differential?  Do teams like that act differently?

The Orioles are performing at a .147 winning percentage above their Pythagorean win expectation. 

For teams performing .070 and above (n=11):
April Wins and Losses - r2 = 0.15
April Run Differential - r2 = 0.24

For teams underperforming between -0.048 and -0.058 (n=11):
April Wins and Losses - r2 = 0.45
April Run Differential - r2 = 0.45

Amazingly, it looks like these metrics work much better for teams in the Yankees' position.  For the Orioles, Run Differential still looks like the preferred method, but Wins and Losses became more interesting.  That all said, I would prefer to use the correlation values from the whole 90 team data set than these sub-divisions because there may be an issue with small sample size.

Regardless, for the Orioles, there does not appear to be a great reason to ignore run differential or prefer team record over run differential for expecting how the team will perform in the future.  Instead, one should probably look to the impact of Chris Tillman and Zach Britton returning, but, of course, that means to ignore the probability of other injuries to key players occurring.

07 August 2014

Runs Scored, Runs Allowed, and the Chase for the AL East Crown

One way that analysts determine whether a team has overachieved is by looking at their run differential and determining their pythagorean expectation. If a team is above .500 but has allowed more runs than it has scored then it safe to presume that a given team has been lucky. If so, it should be possible to use runs scored and runs allowed to determine whether a team has been "lucky" in a specific type of game. For example, does a team win a large percentage of games where they only scores 2 runs?

I decided to build a dataset (accurate as of 8/3/2014) consisting of the 2014 results (W/L, Runs Scored and Runs Allowed) for each game played by the Orioles, Blue Jays, Yankees and Rays. Then I determined how many wins or losses they had when they scored a given number of runs or allowed a given number of runs. This should show us whether a given team excels or struggles in certain types of games.

Using Retrosheet data, I was able to build a control dataset with the results for every team from 2011 to 2013. The Orioles/Blue Jays/Yankees/Rays have won 52.5% of their games so I built a factor into the dataset to account for this. Retrosheet won't release data for 2014 until after the season. I include control percentages where relevant but I'm not sure whether they're helpful for analyzing 2014 results.

This first chart shows how each of the four teams has performed based on runs scored.


This chart shows that these four contenders lose most of their games when they score two or fewer runs, win about half of their games when they score three runs and win most of their games when they score four or more runs. The exception to this rule is Tampa Bay which struggles when they score 3 or 4 runs.

The next chart shows how these teams have performed based on runs allowed.


This chart shows that teams win most of their games when allowing 2 or fewer runs. Toronto, Baltimore and the Yankees win a large number of their games when allowing 3 runs while the Rays have won fewer than half of theirs. These teams win about half of their games when allowing four runs.

It is possible to form groups using these results. This is how the numbers looked grouped based on runs scored. HIS stands for historical results.


The Orioles have either gotten lucky or have shown an ability to win games even when their offense doesn't score. This potentially could be because of the fact that Britton, O'Day and now Miller have been dominant so far this season. It makes sense that a team with a dominant bullpen could win a larger percentage of low scoring games than the average club. While the Orioles have had disappointing results when scoring 5 or 6 runs compared to the 2011-2013 historical data, they have had good results compared to the other three 2014 teams in the sample. The Orioles have struggled when scoring 7 or more runs and should probably have gone 21-2 instead of 20-3 in those games.

The Blue Jays have either been unlucky or have shown an inability to win games when their offense doesn't score 4 or more runs. If they won the expected amount of games when scoring three or fewer runs then they would have eight more wins and would be in first place. This could indicate that they are stronger than they appear or it could indicate that they have a fatal flaw. They have slightly over performed when scoring 4 or more runs.

The Yankees have been about as successful in each of these categories as one should expect.

The Rays have won a larger than expected number of games when scoring between 0 and 2 runs but a lot fewer than expected when scoring between 3 and 4 runs. This was mentioned above and still remains true. If they had performed as expected then they would five more wins. While they would still be in fourth place their position would be much stronger then it is at the present.

This is the same chart but for runs allowed.


The Orioles have underachieved when they have allowed 0 to 2 runs. We've been shut out eight times this season and three were 1-0 losses. This is especially confusing because we've won a higher than expected amount of games when our offense has scored between 0 to 2 runs. We've overachieved when allowing 5 to 6 runs. Luck is another possible explanation.

The Blue Jays have won more games than expected when allowing 2 or fewer runs. This indicates that their offense is pretty powerful and can win games given a strong pitching performance.

The Yankees have underachieved slightly when they have allowed 0 to 2 runs. But they've really struggled when allowing 5 to 6 runs. While they've overachieved when allowing 7+ runs this indicates that they don't have a very powerful offense and are unable to win if their pitching gets blown out.

The Rays have struggled when allowing 3 to 6 runs. While they haven't struggled when allowing 7+ runs this is because teams nearly always lose when allowing 7+ runs. The Rays have had 40 games where they've scored between 0 to 2 runs or about six more then average and 23 games where they've scored between 3 to 4 runs or ten fewer than average. This may have something to do with their problems.

I'm not sure whether this analysis actually is meaningful or whether it is just measuring noise. If it is meaningful then it indicates that the Orioles have had reasonably predictable results this season and that our pitching can win despite some poor offensive performances but that our offense has lost some very winable games. It indicates that the Blue Jays have had major struggles when scoring between 2 and 4 runs and that they likely are fatally flawed and need a significant amount of pitching help. It indicates that the Yankees offense is unable to win when their pitching gives up a large number of runs although this is almost definitely noise. Finally, it indicates that the Rays struggle when they don't receive strong pitching performances.

It will be interesting to see whether these trends continue for the second half of the season.