# NL Cy Young Voting, ERA and FIP Measures

### 2022 NL Cy Young Voting

Last week, Sandy Alcantara won the NL Cy Young Award for the best pitcher in the National League for the 2022 season. It was a unanimous decision as he received all 30 first place votes from the baseball writers. The voting system rewards seven points for a first place vote, four points for second place, three points for third place, two votes for fourth place and one point for fifth place. With this system, the top five pitchers in the voting (MLB article) were Sandy Alcantara, Max Fried, Julio Urías, Aaron Nola and Zac Gallen. What pitching measures were relevant in this ranking of pitchers? Let’s look at some measures (W, L, IP, ERA, FIP, WAR) from the FanGraphs leaderboard.

   Name                W     L    IP   ERA   FIP   WAR
<chr>           <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Aaron Nola         11    13  205   3.25  2.58   6.3
2 Carlos Rodon       14     8  178   2.88  2.25   6.2
3 Sandy Alcantara    14     9  228.  2.28  2.99   5.7
4 Max Fried          14     7  185.  2.48  2.7    5
5 Corbin Burnes      12     8  202   2.94  3.14   4.6
6 Zac Gallen         12     4  184   2.54  3.05   4.3
7 Yu Darvish         16     8  194.  3.1   3.31   4.2
8 Logan Webb         15     9  192.  2.9   3.04   4.2
9 Tyler Anderson     15     5  178.  2.57  3.31   4
10 Jose Quintana       6     7  165.  2.93  2.99   4
11 Joe Musgrove       10     7  181   2.93  3.59   3.5
12 Merrill Kelly      13     8  200.  3.37  3.65   3.3
13 Julio Urias        17     7  175   2.16  3.71   3.2


I believe most voters looked primarily at a pitcher’s ERA in their decision-making. Julio Urias’ 2.16 and Sandy Alcantara’s 2.28 are the leading ERAs in this group, but Urias only pitched 175 innings compared to 228 for Alcantara. I think Alcantara’s unanimous decision for the Cy Young was likely due to his low ERA for the large number of innings pitched.

But there is an issue with the use of ERA in measuring pitching performance. The number of earned runs allowed is really a function both of the pitcher and the team’s defense. There has been a recent effort among sabermetricians to construct alternative pitching measures focusing on outcomes like walks, strikeouts and home runs allowed to remove the effect of the defense. This has led to the use of the FIP (fielding independent pitching) measure defined in the FanGraphs sabermetrics library

$FIP = \frac{13 \times HR + 3 \times (BB + HBP) - 2 \times HR}{IP} + FIP Constant$

What the advantages of FIP over traditional pitching measures like ERA? The general motivation between FIP is the observation that pitchers really have little control over the results of balls put into play. So we remove those balls in play from the measure and focus on the outcomes that the pitcher has complete control like strikeouts, walks and home runs allowed. It is generally believed that FIP is a more stable measure than ERA and actually can be a better predictor of next season’s ERA than the current season ERA.

Using the FIP measure, the top two pitchers in our group were Carlos Rodon (2.25) and Aaron Nola (2.58). But Nola was only ranked 4th in the Cy Young voting and Rodon was 6st. Note also that Nola and Rodon were ranked first and second with respect to the WAR measure in the FanGraphs leaderboard.

In this post, I will provide evidence to show that FIP is indeed a more stable or consistent measure than ERA.

### Aaron Nola’s Performance Across Seasons

We begin by graphing Nola’s ERA and FIP measures over the eight seasons that he has pitched in the Major League. Looking across seasons, Nola’s ERA and FIP values generally average about 3.3-3.5. But it is pretty clear that Nola’s FIP measures are more stable than his ERA values across seasons. A quick calculation gives that the standard deviation of his FIP values is 0.499 compared with a standard deviation of 0.778 for his ERA values. It is interesting that Nola’s ERA was much higher than his FIP in the 2016 and 2021 seasons — this might be attributed to the Phillies poor defense during these particular seasons.

### Pitching Career Trajectories of FIP and ERA

If FIP is indeed a more stable measure of pitching performance than ERA, one would think this would be evident if one looked at the careers of some of the great pitchers in MLB history. Generally, pitchers have a standard shape of their career trajectory — a pitcher will improve through mid career and then decrease in performance until retirement. One can represent his trajectory by a least-squares quadratic fit — we look at these trajectories from using ERA and FIP as performance measures. By looking at the deviations (residuals) from the fit, we can see if the residuals from a fit using FIP are indeed smaller (that is, more stable) than the residuals from a fit using ERA.

I focus on all pitchers in MLB history whose midyear was between 1970 and 1990 and pitched at least 3000 innings — there were 29 pitchers satisfying these criteria. Below I display each pitcher’s ERA as a function of his age with smoothing quadratic fits on top. The shaded regions show the standard errors of the fit for each trajectory. I repeat this exercise using the FIP measure.

In the Aaron Nola example, I measured stability by looking at the standard deviation of the season ERA or season FIP values and saw that the FIP values had the smaller standard deviation. When we are fitting a pitcher’s season-to-season measures by use of a quadratic curve, one can measure stability by use of the residual standard deviation $S$. (Note that $S$ is the estimate of $\sigma$ in the model $y = a + b Age + c Age^2 + \sigma$.)

For each of the 29 pitchers, I collect the residual standard deviations for the quadratic fits to the season ERA and FIT values. I show a scatterplot of the ERA standard deviations and the FIP standard deviations with a line y = x drawn on top. Most of the points fall below the line, indicating that the residual standard deviation tends to be smaller for the fit to the FIPs than for the fit to the ERAs. In other words, the season FIP values for a particular pitcher tend to be more stable across seasons than the corresponding season ERA values.