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Tangotiger Blog

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Wednesday, September 25, 2024

Runs Above Average

By Tangotiger 12:00 AM

It is very (very very) simple to figure out Runs Above Average (RAA) for a pitcher. I'll use Paul Skenes as the example.

Take the league average ERA (4.086) and subtract our pitcher's ERA (1.992). That makes Skenes 2.094 runs per 9 IP better than league average.

Since Skenes has 131 IP, we take the above number (2.094/9) and multiply by 131 to give us +30.5 runs above average.

That's it. That is Runs Above Average using ERA-only. That figure for Skenes is 4th highest in MLB, behind Sale (+34 runs), Skubal and Wheeler (+33).

Now, you may be asking: what about park factors? Baseball Reference has Skenes as pitching in slightly batter's parks. So, that simple league average of 4.086 is actually too simple, since that figure is the same for all pitchers. We know that can't possibly be true. Skenes also faces tougher competition than average. Skenes supposedly has weaker fielding support than others. When you make all these adjustments, Skenes actually ends up being +41.5 runs above average. Remember, unadjusted he was at +30.5 runs above average. So, the adjustments gives him an extra +11 runs. That's right, his 1.99 ERA is actually NOT giving him enough credit.

Since Baseball Reference is terrific in how they share their data, it's really quite simple to compare the ERA-only RAA to the fully-adjusted RAA they provide.

On this chart (click to embiggen), on the x-axis is the ERA-only RAA. If you don't want anything adjusted and you just want to rely on ERA, then just look at those numbers.

The y-axis is the bonus (or deduction) you have to apply to your pitcher to account for the context that they end up pitching in. Skenes for example is in the right corner, at 30; 11. That means his ERA-only RAA is +30 runs, and he has a +11 run bonus for his context. So, he's worth +41 RAA.

Some pitchers get FAR more bonus than that. Hunter Greene gets +19 runs of bonus for his context. That means his ERA is really clouded, practically Coors-like in its effect. So, he's +20 runs for his ERA-only and another +19 runs for the context, for a total of +39 RAA.

Erick Fedde is +13 for his ERA-only, and another +17 for his context, giving him +30 runs above average.

We can compare Cy Young candidates Cole Ragans (+17, +10) to Logan Gilbert (+18, -10). You see, both are very similar based on their ERA. But according to Reference, Ragans faced a tough context, while Gilbert had a pretty easy context. That's a 20 run gap between the two in terms of their context. So Ragans ends up being +27 while Gilbert is only +8.  In other words, instead of Ragans being 1 run behind Gilbert, he's 19 runs ahead, all because of the 20 run difference in their context.

Now, there's no question that if you are a Mariners fan, you will disagree, and a Royals fan is quite happy. That's unfortunately how these contexts gets interpreted: how does it affect MY player.

Chris Flexen is one of the worst pitchers in baseball using ERA, at -18 runs.  But Reference says he also had one of the toughest pitching environments to the tune of +17 runs.  So overall he ends up being practically league average at -1 runs from average.

Did Chris Bassitt have an ordinary season (-1 RAA)?  Or did he have one of the easiest contexts in all of baseball (-15 runs) so that he actually had a disastrous season (-16 RAA leading to -0.1 WAR)?

By ERA, Bassitt is 17 runs better than Flexen.  By fully-adjusted Reference method, Flexen is 15 runs better than Bassitt.  One had an average season, one had a disastrous season. And which pitcher had which is based on whether to fully trust ERA or to fully accept the adjustments.

Reference lays it all out there for you so you can see what they are doing. You either buy it or you don't. But the transparency is something to be commended.


WAR
#1    Rally 2024/09/25 (Wed) @ 09:01

White Sox pitchers are at a clear disadvantage compared to pitchers on other teams - they never get a chance to face White Sox hitters.


#2    Tangotiger 2024/09/25 (Wed) @ 10:08

It was slightly more effort to do this for Fangraphs, but I’ll explain the process.

1. Since Fangraphs doesn’t provide the Runs Above Average, we need to reverse engineer their WAR to get there.
2. We need a Runs Per Win converter, and Fangraphs which basically uses the form I’ve proposed.  Chris Sale get 8.1 RPW for example, while bad pitchers are above 10.
3. Convert WAR to RAR by multiplying by RPW. Sale’s 6.43 WAR becomes 52.2 RAR
4. We convert RAR to RAA easily enough, by using the average RAR per 9 IP (which is 0.80).  Sale gets a RAA of 36.5.
5. We compare to his ERA-only based RAA of 33.6.
6. Sale gets a +3 run boost using the Fangraphs-approach

The x-axis is like above, the ERA-only version.  Then the y-axis is the Fangraphs bonus/deduction by essentially stripping away everything that you know about ERA and focusing on FIP.

Paul Skenes for example has a 1.99 ERA, but “only” a 2.48 FIP (2nd in the league behind Sale).  As a result, his baseline using ERA of +30 runs is adjusted downward by -7 runs using the FIP-centric version that Fangraphs applies.

With a very FIP-centric view here, it’s a paradigm shift to essentially discard everything you know about ERA and stick with those components that the pitchers are most linked to (SO, BB, HBP, HR), as well as another adjustments Fangraphs applies (infield popups I believe).

Using ERA as a baseline allows you to strip things away, but it’s unclear if enough is being removed, or if it is removed in the best manner.

On the other hand, using FIP as a baseline gives you a strong center but then ignores everything else that the pitcher could be contributing.

So, FIP is on solid ground, but doesn’t allow for too much expansion.  ERA is on shaky ground and tries to stabilize itself.

The reality?  It’s somewhere between the two.  You can try to figure it out.  Or, take advantage of the two extreme positions, go 50/50 and get on with your day.


#3    Tangotiger 2024/09/25 (Wed) @ 10:16

You will notice something interesting, a bias.

However, this bias is not a FIP-bias but an ERA-bias.

You see, a low ERA will have more good luck than bad luck.  While a high ERA is reverse: more bad luck than good luck.

It is an OUTPUT metric, and as a result subject to Random Variation. 


#4    Rally 2024/09/25 (Wed) @ 23:00

Here’s a great illustration of the difference between fWAR and bWAR.

John Candelaria 1977 vs Dwight Gooden 1990

Doctor K 3.83 ERA, 2.44 FIP, WAR 2.5/6.8
Candy Man 2.34 ERA, 3.90 FIP, WAR 7.4/2.9

Both around 230 innings. Candy won 20, Doc 19.


#5    Bobby Mueller 2024/09/28 (Sat) @ 21:57

The fWAR, bWAR debate with pitchers always makes me think of Pedro Martinez in 1999 and 2000.

1999
213.3 IP, 2.07 ERA, 1.39 FIP, 2.36 RA/9
11.6 fWAR
9.8 bWAR

2000
217.0 IP, 1.74 ERA, 2.17 FIP, 1.82 RA/9
9.4 fWAR
11.7 bWAR

Both were terrific seasons, but with a 2-WAR difference between how FanGraphs valued them versus B-Ref.


#6    Rally 2024/09/28 (Sat) @ 22:03

I never even noticed it at the time, just a few years later when Voros mentioned it when he was first explaining DIPS.

Pedro struck out so many that even with a high Babip, he gave up very few hits. In fact, despite the .325 babip, he led the league in fewest hits per 9 innings.


#7    Tangotiger 2024/09/28 (Sat) @ 22:34

Bobby: terrific!

While I was very aware of the BABIP and cite it often, I never noticed the overall/split as you showed it, great stuff!


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