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Lesson 1: Advanced Stats for Pitchers

JM Tohline (JMToWin)

JM Tohline (Tuh-lean) – DFS alias JMToWin – is a novelist and a DFS player who specializes in high-stakes MLB and NFL tourneys, with a strategy geared toward single-entry play in multi-entry tourneys. He joined the DFS scene at the beginning of the 2014 MLB season, and has since won five DFS championship seats and two separate trips to the Bahamas. His tendency to type a lot of words leads to a corresponding tendency to divulge all his DFS thoughts, strategies, and secrets…which is exactly what he does in his RotoGrinders articles and RotoAcademy courses. You can find JM on Twitter at JMToWin.

Stats!!!!

Seriously. Stats deserve those four exclamation points in MLB, as stats are the fundamental basis of our MLB DFS play. Stats tell us nearly everything we need to know. Stats are gold.

Below, you will find the most important statistics I look at for pitchers – and an idea of how each of these pieces fit into the puzzle.

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K%

K% (strikeout rate) is the percentage of batters faced that a pitcher strikes out. League average fluctuates from year to year, and from era to era, but it’s always safe these days to simply peg “league average strikeout rate” at 20%. (If you want to be more specific: it was 21.1% in 2016.) This gives you an idea of how far above or below the league average a pitcher sits.

In MLB DFS, strikeouts are king in pitcher scoring. Strikeout upside = fantasy scoring upside.

Hint: Do not just pay attention to a pitcher’s strikeout rate! Also pay equal attention to the strikeout rate of a pitcher’s opponent. If a pitcher with a league-average strikeout rate is facing a team that strikes out at a rate significantly above the league average, you can expect that league-average strikeout pitcher to notch strikeouts against this team at a rate significantly above the league average. Always remember: When a team, as a whole, has a strikeout rate of X% a month or more into the season, this essentially represents what the team does against the average pitcher they face. And over time, the average pitcher they face will essentially amount to the league-average pitcher. Read that a few times if you have to, as it’s one of the most important elements to grasp in MLB DFS. By understanding that a pitcher’s stats over the long run, or a hitter’s stats over the long run, essentially come against a league-average opponent, you can then set your daily expectations against the league average. A pitcher with a league-average strikeout rate of his own, facing a team that strikes out 25% of the time (to put that another way: that strikes out 25% of the time against the league-average pitcher!), would actually see his strikeout rate climb to 25% if he could face just this one team all the time. Talk about a great way to identify pitchers who are overvalued or undervalued on a slate!

Hint 2: Always sort team stats by handedness! There are teams, for example, that strike out 16% of the time against right-handed pitchers, but strike out 25% of the time against lefties. On the surface, their team strikeout rate will appear to be somewhere close to the league average…but by digging deeper, you’ll discover that this team heightens strikeout expectations for southpaws, and hampers strikeout expectations for right-handed pitchers.

Hint 3: Swinging strike rate (SwStr%) is the percentage of pitches thrown that result in a swinging strike (i.e., a “swing and miss”). Generally, SwStr% will be just a bit below half of a pitcher’s strikeout rate (or thereabouts). So where the league average K% in 2016 was 21.1%, the league average SwStr% in 2016 was 10.1%. There are exceptions to this, and the best way to spot these exceptions is to look at a pitcher’s history (if a pitcher has a career SwStr% of 10% but a career strikeout rate of 24% – over a stretch of multiple years – this can be considered sustainable). SwStr% is a great way to identify short-term outliers in performance. If a pitcher’s strikeout rate has dropped but his SwStr% has held steady, you can expect the strikeouts to jump back up soon. Conversely, if a pitcher’s strikeout rate has spiked but his SwStr% has not, you can expect him to regress to the mean.

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BB%

BB% (“base on balls percentage,” or “walk rate”) is the percentage of batters a pitcher walks. League average is generally around 8% (8.2% in 2016). Walk rate is a great way to get an idea of how safe and consistent a pitcher is.

BB% is probably about 2% of the equation for me on any given day, but it’s easy to glance at, and it’s something I always pay attention to. All other things being equal, a pitcher with a 5% walk rate is a much safer (and a better cash game) play than a pitcher with a 10% walk rate. More walks mean more base runners, which leads to more opportunities for big innings. More walks also indicate less overall control, and often lead to shorter outings.

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SIERA

SIERA is an ERA predictor. Essentially, SIERA tells you what a pitcher’s ERA should be – and likely will be over time – based on a combination of his underlying numbers (xFIP does the same thing – though SIERA does it slightly better). If a pitcher has a 3.00 ERA, but a SIERA of 4.00, you can generally assume – right away – that the pitcher is both overpriced and overvalued by the DFS community, as his recent performances surely look good on paper, and in DFS box scores, but his real-life expectations are quite different. Conversely, a pitcher with a SIERA of 3.00 but an ERA of 4.00 is going to be underpriced and undervalued by the DFS community. SIERA is no longer a secret weapon; everyone knows about it and understands it. But the masses do still end up trusting “recent performance” too much – even when they can see, from SIERA, that recent performance is not sustainable. This gives you an edge when you listen to the numbers!

Hint 1: SIERA does not account for hard-hit/soft-hit data (more on these below). A pitcher who induces lots of soft contact will usually have a SIERA that says his ERA should be higher – but in these cases, we expect SIERA to be a bit off, and should value accordingly.

Hint 2: SIERA normalizes fly balls – setting them to the league average HR/FB (home run per fly ball) expectations. Pitchers who induce tons of fly balls, or who play in a ballpark that suppresses home runs, tend to have a lower HR/FB rate than the average pitcher. As such, these pitchers will always have a lower ERA than their SIERA says it should be. Again: this is sustainable, and we should adjust accordingly.

Hint 3: All of this data, for every pitcher, can be found on his Fangraphs page. You can customize your home page for pitchers to show the stats you want to see, and you can check out a pitcher’s “Splits” page to find most of the remaining information you need.

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Hard / Soft

League-average hard-hit balls: 31.4% (2016 data)

League-average soft-hit balls: 18.8% (2016 data)

These numbers actually fluctuate fairly heavily from year to year, but here is an idea of the general range in which these numbers sit:

Hard-hit numbers from 2013 through 2016: 29.9%, 29.1%, 28.8%, 31.4%

Soft-hit numbers from 2013 through 2016: 16.5%, 18.3%, 18.6%, 18.8%

Hard and soft contact is now something that is widely looked at by MLB DFSers – but it is still underutilized as a tool in rostering underpriced arms. (More on this in a bit.) For now, know: guys who induce a lot of soft contact are far less likely to crash into a horrible outing, whereas guys who allow a lot of hard contact are more likely to crash into a horrible outing.

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Etc.

Know your pitchers! You won’t learn everything at once. You won’t commit it all to memory. But each day – through the “time condensing” article you read, and through the extra research you do – make an effort to actively pick up tidbits about various pitchers. Is this guy a fly ball pitcher or a ground ball pitcher? Does he induce a lot of infield fly balls? Can he get strikeouts when he has to? How does he get his strikeouts? Is he bad at holding runners on base? Does he implode if a few things go poorly in the first inning? – if a ground ball goes through his shortstop’s legs, is this going to ruin his approach for the rest of the night?

Know your pitchers! And profit as a result.

About the Author

  • JM Tohline (JMToWin)

  • JM Tohline (Tuh-lean) – DFS alias JMToWin – is a novelist and a DFS player who specializes in high-stakes MLB and NFL tourneys, with a strategy geared toward single-entry play in multi-entry tourneys. He joined the DFS scene at the beginning of the 2014 MLB season, and has since won five DFS championship seats and two separate trips to the Bahamas. His tendency to type a lot of words leads to a corresponding tendency to divulge all his DFS thoughts, strategies, and secrets…which is exactly what he does in his RotoGrinders articles and RotoAcademy courses. You can find JM on Twitter at JMToWin.

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