2021 is over and we turn the page to 2022. Here are my current ranks for the starting pitching position for 2022. I will post updates as things change with signings, injuries, or news in Major League Baseball.
See my other ranks here: https://fantasy.fangraphs.com/category/rankings/
We all look at underlying metrics when it comes to any player, but whether we like to admit it or not we still sometimes look at the basic numbers. For pitchers, while ERA and WHIP of course mean something, we all know they aren’t predictive at all. Yet sometimes they are hard to ignore and we think, “well he did have a great ERA.” As a fun little exercise, we are going to compare two pitchers at a time based on underlying statistics to see if we can push away some biases. It’s time for part two, let’s have some fun!
I completed the counting stat analysis on early March pitcher counting stats after finishing the hitter projection comparisons (part 1, 2, 3, 4, and 5). it is time for the pitchers to take center stage. For the first article, I’ll measure the accuracy of counting stats from early March. The results were mixed this time with the aggregators having a decent showing.
First, here are the projections analyzed.
To create a list of players to compare for accuracy, I took the TGFBI ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »
Yesterday, I reviewed my comparison of starting pitcher spring training strikeout rates to Steamer projected strikeout rates and the list of pitchers with potential strikeout rate upside this past season. Now, let’s review the list of potential walk rate improvers based on the gap between spring walk rate and Steamer projected walk rate. Was Spring the first hint at an improved walk rate than expected? Let’s find out.
Recently I published my Peripheral Prospect hitters for 2021. In a perfect world I would have published my thoughts on a wider array of hitters periodically throughout the season. Alas, this is not a perfect world, so I settled for a year-end catch-all post.
Rinse and repeat for pitchers. The rules: (1) They pitched in the high minors (Double-A or Triple-A) but not the MLB level, and (2) they cannot be featured on any prominent top-100 list. Top-100 updates count (and all due respect to those updated lists, because revising your priors is not a bad thing!).
Brace yourselves: I’m kicking this off with three Cleveland farmhands with whom I implore you must familiarize yourself. Eight Peripheral Prospect pitchers for your fine Wednesday:
At the end of March, I shared a list of starting pitchers who posted the largest gaps between their Spring K% marks and their Steamer projected K% marks. A many years old study I conducted confirms that Spring strikeout rate does convey meaning, so while 99% of Spring stats should be ignored, strikeout rate isn’t one of them. Small sample sizes and uneven competition caveats still heavily apply, though. Let’s find out how these Spring strikeout rate surgers actually performed. How many, if any, beat their Steamer projected strikeout rates?
Yesterday, I reviewed the list of eight starting pitchers my Pod Projections were more bullish on ERA than Steamer. Today, let’s now flip to the starting pitchers I was more bearish on, suggesting downside versus Steamer. Overall, the Steamer ERA projections were more pessimistic than mine, so the gaps between the upside pitchers were much larger than the gaps between these downside pitchers. That means this list is more of a crapshoot. Let’s check out the results.
Now that the analysis hitter projection comparisons (part 1, 2, 3, 4, and 5) are done, it is time for the pitchers to take center stage. For the first article, I’ll measure the accuracy of counting stats from early March. Razzball had a near clean sweep as it only missed on Saves.
First, here are the projections analyzed.
• Steamer (FanGraphs)
• ZIPS
• DepthCharts (FanGraphs)
• The Bat
• Davenport
• ATC (FanGraphs)
• Pod (Mike Podhorzer)
• Masterball (Todd Zola)
• PECOTA (Baseball Prospectus)
• RotoWire
• Razzball (Steamer)
• Paywall #1
• Average of the above projections
To create a list of players to compare for accuracy, I took the TGFBI ADP (players in demand at that time) and selected all the pitchers in the top-450 drafted players (30-man roster, 15 teams in the Main Event) in ten or more leagues. Then I removed all the pitchers who never threw an inning. Read the rest of this entry »
Last week, I reviewed my hitter Pod Projections vs Steamer projections comparisons. Let’s now move along to the starting pitchers and ERA. As a reminder from my original post:
Though Steamer is the best pitching projection system out there, it struggles on pitchers that have shown consistent BABIP and HR/FB rate suppression skills and deficiencies, as what usually works for the majority of pitchers — projecting a heavy dose of regression to the MLB mean — means it misses on those uncommon exceptions. I use Statcast’s xBABIP now for my projections, so I’m not afraid to forecast a mark that strays from the league average. However, I certainly still include some regression as we don’t always have enough batted balls in a pitcher’s history for that mark to stabilize.
We all look at underlying metrics when it comes to any player, but whether we like to admit it or not we still sometimes look at the basic numbers. For pitchers, while ERA and WHIP of course mean something, we all know they aren’t predictive at all. Yet sometimes they are hard to ignore and we think, “well he did have a great ERA.” As a fun little exercise, we are going to compare two pitchers at a time based on underlying statistics to see if we can push away some biases. Let’s have some fun!
