Archive for Meta Analysis

Changing a pitcher’s pitch mix seems to be the newest path to success. Having a pitcher utilize his two to four best pitches can help him focus his arsenal for peak results. Finding these pitchers can be a huge advantage and the great and wonderful Eno Sarris used the original work to find Carlos Carrasco. I’m going to step an owner through the procedure using a few examples from the news so they can find their own diamond in the rough.

The basic idea behind changing a pitcher’s pitch mix is to have them throw as many effective pitches as possible. The original studies focused on above-average pitches. This is a simple method and one I use when examining a pitcher. The pitcher’s pitch results can be found by going to their page at FanGraphs, clicking o the Splits tab, then the Pitch Type Splits tab (example).

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The introductory section below is going to be similar to previous LABR recaps since little has changed and there’s no sense in rewording things.

The clearest sign of a new baseball season is the annual super early LABR Mixed draft. Last Tuesday, 15 of us fantasy nerds virtually gathered to speculate where the swath of still-free agents will sign and hope our early picks don’t suffer spring training injuries. Though I’m certainly not a fan of February drafts, at least it provides me the needed motivation to finish my first run of Pod Projections that drive my player values. Without the forecasts and valuation spreadsheet, I’d be drafting blind, and that’s no blueprint for a Yoo-hoo shower.

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A few days ago, many of the best fantasy baseball minds got together for the 15-team mixed LABR draft. While several storylines existed with it, the running joke in the draft chat was it if Mike Podhorzer or Scott Pianowski would blink first and draft a pitcher. Mike did and took Jake Arrieta in the 7th round while Scott held out until the 12th. While these two owners obviously took accumulating hitters to an extreme level, other owners took a more balanced approach or bought into pitching. Finding a hitter-pitcher balance, especially in a draft can be tough to calculate on the fly. The following a simple tool to help with that in draft confusion.

The first concept to understand is that the talent is not evenly distributed. I went over this concept earlier this week showing the non-linear talent drop using auction prices. Using concepts from this analysis can help create a simple framework by giving each round its own auction value. This can be done by going to our auction calculator, entering your league settings, and downloading both the hitter and pitcher values.
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On Tuesday, I hopped over to the pitcher side of the ledger to discuss nine fantasy relevant starting pitchers with strikeout rate upside this season. I used my xK% equation and compared what the formula spit out to what the pitcher’s actual strikeout was. Today, I’m going to share the ten pitchers who most outperformed their xK% marks.

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Major league talent drop is not linear and it shouldn’t be treated as so in fantasy baseball but it does for many owners because we love our simple ordinal rankings. The talent drop from the best shortstop to the 10th is more than the drop from the 10th to the 20th. As soon as an owner moves away from using just rankings and goes to an overall production value they will gain a leg up on the competition. The whole idea can be explained by the talent distribution curve.

The talent distribution curve can be created with auction dollars. Other values can be used such as raw Standings Gain Points. But setting our auction calculator to the standard 12-team roto settings. Here is the talent distribution curve:

Note: While only 276 players are needed for this example, I extended the data out for deeper leagues. For those in 12-team -Only leagues, they will be picking at the 552 mark where the talent really begins to drop.

I’m not even sure if it has a shape. Maybe a sideways ‘S’. There is an extreme drop for about 100 picks and then starts to level off. Owners want to acquire as many players in this first group. In this part of the draft, talent needs to be prioritized over a position since talent differences exist more at this level.

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We all know a park’s dimensions, foul territory, hitter’s backdrop, atmospheric effects, etc. play a significant role in shaping our projections and on a player’s performance. Collectively, we know these effects as park factors. We are probably most aware of a park’s home run park factor. I’m sure that for many parks, you have a perception in your mind as to its home run friendliness. The data might say otherwise, but at least you think you know, unlike, say, triples, which I’m sure most haven’t a clue which parks are best for boosting the three-bagger. Unfortunately, while the idea of park factors is sound, they are extremely problematic to rely on.

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After two days discussing individual players with apparent upside and downside given their fly ball pull percentages (FB Pull%), it’s time to really get to know the metric. Let’s begin by looking at the leaguewide trend over the last 10 years.

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Yesterday, I began my discussion of another one of my xHR/FB rate equation’s components, FB Pull%, and shared the hitters who posted above average Brls/True FB and Avg FB Dist marks, but below average FB Pull% rates, hinting at upside if a change in approach is made. Today, I’m going to check in on the hitters who posted below average Brls/True FB and Avg FB Dist marks, but above average FB Pull% rates. These hitters are at greater risk of HR/FB rate regression given their heavy reliance on pulling their flies. If that skill erodes or approach is altered, there would be major downside.

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It’s tough to create a perfectly balanced team on draft or auction day. Owners are feeling the push-and-pull of trying to balance all five categories in a roto league. Mid-draft, many owners decide to drop a category with the hope of finding the needed stats on the waiver wire. Knowing which stats can be found can be tricky. By looking back at last season’s Tout Wars leagues, a decent idea of available stats can be determined.

One feature of the OnRoto.com fantasy league website computes the league’s final standings using just the drafted teams (nine pitchers, 14 position players). I took these draft values and compared them to the actual final values for each of the four roto leagues (12-team AL and NL-only and the two 15-team mixed leagues).

Some specific notes on these leagues. First, they are deeper than most leagues so every player who might be good is already owned. As for the timing of the mixed draft (the other three were auctions) happened a few weeks before the other three. Additionally, only the 23-man rosters were used used for the projected standings. Each team had an additional five or six-person bench.

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One of the components of my new xHR/FB equation is fly ball pull percentage (FB Pull%). Sadly, I have spent significantly more time discussing barrels per true fly ball and average fly ball distance, so I’m going to change that. Let’s talk FB Pull%!

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