For those of you old enough to remember a time before Survivor and American Idol, you might recall the era of Kaizen that permeated the economies of the 1980s and 90s. In Japanese, kaizen means something like “continuous improvement,” and it was one of those old pre-capitalist ideas that got co-opted by industrial society. So instead of like, running a bit farther every day or being 10% happier, the concept of kaizen turned into this phantasm of continual product improvement and personal productivity maelstroms. Maybe you’re running faster, but it’s because your job needs you to finish your work and somebody else’s work at the same time. Product sprints. Agility. Synchronicity (and not the album by The Police). But “continual improvement” done in the name of producing things faster, doesn’t necessarily mean that the actual product is any better.

It’s really not a surprise that the fantasy sports world also adopted this kaizen mentality — more products, somehow “improving,” but ultimately making fantasy players work harder. How many people are old enough to remember when a copy of Baseball America and a printer was the complete setup to play fantasy sports? Now we’ve got data providers everywhere. How many accounts do you have with a data provider? I’ve got [thinks for a while] six? I’m still learning about fantasy analysis sites that I’ve never heard of before, and I’ve consumed fantasy content on the regular since Firefly was on actual broadcast TV. And which provider is better? Is it the one that outputs data the quickest? Is it the one that makes you laugh? Is it the one that uses the least amount of preface to their articles?

All this to say: I’ve “improved” my system a bit this week. Is it actually better? Who knows. I worked on it, I’ll tell you that much. I used best practices and data-backed principles learned from years of study. I had a “Hypeonator” that said if a player was “Hype” or “Whack” and then I deleted it. I merely open doors — it’s up to y’all if you take the hype. That said, let me share a bit of the terminology that I’ll be bringing to the fore for the upcoming articles.

Example Table:

Name: As names have power, words have power.

L30$/G: This is the $/G over the Last 30 days, as calculated by Rudy over on this side of the Razzball site. This is the actual fantasy value for the player, regardless of how the player should be playing. Remember when I pointed out last week that Tyler Mahle and Alek Manoah had basically the same advanced stats but were 200 spots apart on the Player Rater? This $/G is showing you precisely how useful this player is for your fantasy team. Note: I did not say this value means the player is good. There are plenty of players who are useful for your fantasy team who are not good. In the above example, we see Carlos Rodon is extremely useful for your team with his positive value, and Patrick Corbin is not useful at all for your fantasy team.

wK/9: Weighted K/9, calculated to show how many more or less strikeouts the pitcher is throwing compared to the field. 9 K/9 sounds great, right? Except it’s actually below average for your top pitchers right now. Rodon above has a 21.26% higher K/9 rate than the top 50% of MLB pitchers right now. Meanwhile, Patrick Corbin has a K/9 rate 25% lower than the top 50% of MLB pitchers right now. I calculate this on a total season basis; there is little recency bias here.

wPABIB: PABIB sounds like Mr. Pibb, right? So, BABIP is Batting Average on Balls in Play, and we all know it from the movie Moneyball, right up there with Brad Pitt. Problem is, at least in my world, is that BABIP tells us the batting average on balls that are hit into the field. I’m a bit more interested in how many outs are generated by balls hit in play. This is simply the inverse of BABIP. I do this because the quality of hits are a significant generator of BABIP; it’s not simply “hit it where they ain’t,” but “can’t catch what speeds by you at 110MPH.” I then weight PABIB, taking the top 50% of the field into account, and express the output as a percent. In the above example, Carlos Rodon is 1.55% better at generating outs than other pitchers, and Patrick Corbin is 6.55% worse than the field.  I calculate this over the last 30 days so there is a fair amount of recency bias, but it’s also responsive to things like warmer weather and if MLB changed the balls again.

wL30FIP: This is the weighted Fielding Independent Pitching stat over the last 30 days. FIP isn’t really predictive over this sample, but it gives us a reasonable approximation if a player should be doing better than their usual fantasy stats indicate. For the weighting in this sample size, I only use the top 60 pitchers (a standard 12-team fantasy league deployment of 5 starters per team). In the example, Carlos Rodon’s FIP over the last month has been 62% better than the field. Patrick Corbin’s FIP is 14% better than the field — he should be doing better than we’re seeing. In fact, he’s 14% better than the top 60 pitchers — that’s gotta be worth something down the line, right? Maybe let’s talk about that in the meat of the article, shall we?

News and Notes

Patrick Corbin: My system really likes Patrick Corbin and keeps throwing him into Tier 2, despite the fact that Corbin is basically at the bottom of the Player Rater (indeed, he’s ranked at nearly 1100). What gives? He’s 0-5 with a 6+ ERA. That’s fantasy trash. Thing is, we can’t play fantasy based on what’s already happened. His season-to-date true skill stats are plenty fair: his FIP is 3.38, SIERA is 4.42, and he’s allowed 1 homer on the year over 30 innings. Over the last 20IP, he’s got a 2.37 ERA and, 2.56 FIP — nothing wrong with that. He’s allowing barrels but most of them are going at the ground. For most fantasy managers, you still don’t want to use this particular Corbin (try the Burnes flavor), but there’s going to be a big DFS winner soon who has a $5000 Patrick Corbin that gets the regression coming his way.

Jesus Luzardo: [post-submission edit: he’s off to the IL!] How about we do that same “last 20 IP” comparison with Luzardo as we did with Corbin? Over Luzardo’s last 20IP, he’s got a 3.66 ERA, 5.12 FIP, 6 BB/9 and has allowed 4 homers. This is the thing about public perception: people kept asking me why Corbin was so high on my list, but nobody asked me why Luzardo was also high — in fact, chasing that tier 1. Asking isn’t a bad thing; as humans, we’re somewhat predisposed to question certain beliefs and let other beliefs go unquestioned (Hey Siri, define “confirmation bias.”). To start the season, all of us analysts were in awe of Luzardo’s re-invention in Miami, and now I’m starting to wonder if it was a Miami Miracle or just a David Blaine stunt. My system loves Luzardo’s K/9 rate — and we all should love a great K/9 rate. Strikeouts are the biggest predictor of future success for pitchers. Full stop. But pitchers can’t walk 6 batters per nine and allow nearly 2 HR/9 and expect to survive. You know who did that in 2020? Robbie Ray. Hmm…maybe there is room for resurrection. There’s room for a Luzardo re-awakening, but don’t be surprised if he turns into a pumpkin quickly.

Reid Detmers: No, hitter! Detmers came this close to a perfect game. Still, it was, as the record books go, pretty ugly from a boxscore standpoint. 9IP, 2K, 25 batted ball events. In baseball, you’re either safe or out. One of two choices, right? Imagine flipping a coin 25 times and each time it comes up heads. Out! Now do that 25 times in a row, except every time you flip the coin, your friend has to catch it and still land heads. No-hitters tend to be filled with strikeouts because it’s so difficult for zero batted balls to end up as a hit. FanGraphs reports that Detmers’ no-no featured the fewest strikeouts in a no-hitter since 2011. On the season, Detmers’ K rate is waaaaaaaay (manage that, spell check!) down compared to the field, and his FIP — even after the no-no — stands at 4.07. Elephant-minded readers recall that Alec Mills and his 86 MPH fastball threw a no-hitter in 2020, and Detmers’ no-no has that kind of feel to it. Even Mills managed to K 5 batters, though. Detmers is an OK try for 12-team leagues, but his fantasy value is mostly tied to his prospect status. He’s a top 50 prospect and only 22 years old, so the best is yet to come — it just likely won’t come in 2022.

Paul Blackburn: The top of my Confidence ranking is filled with the top strikeout artists in the league. After Pablo Lopez, we see the “median” pitchers for K-rate. And then comes Paul Blackburn, who has a precipitous lack of strikeouts but is somehow toward the top of the rankings. Why? His impressive ability to limit batted ball damage and walks. All of his true skill stats are below 3.0 runs allowed, he’s got 4 wins and 37 IP, and a WHIP below 1.00 thanks to that. A lot of fantasy-relevant pitchers have done this kind of performance for a year and had great success — Marco Gonzales, Hyun-Jin Ryu, and Zack Greinke have all had top 20 fantasy years with this kind of profile (and all have finished in the top 10 SP, with Grienke doing it multiple times). Blackburn’s 7.0 K/9 won’t matter much if he approaches 200 IP, because his total strikeouts will be equivalent to a 9+ K/9 pitcher who throws less IP. Keep starting.

Luis Castillo: Small sample size (9IP) but he’s looking rough so far. Don’t start until he figures things out. Last year, it took him until the All-Star break to feel comfortable, and thereafter he was brilliant for fantasy. I’m not saying drop, but definitely don’t trot him out there every 5 days right now.

Glenn Otto: Otto had a nice showing last year but is approaching 20IP with a 6+ FIP this year. He’s walking nearly as many batters as he’s K-ing, so let him sit even in deep leagues.

Mike Clevinger: Gonna be in the “you do you” category for a while. He’s failed to top 5 IP in each of his starts (expected), has a BB/9 above 5 (unexpected), and a 5/4 ERA/FIP (what is this, Dave Brubeck?). You figure out if you want to start him or sit him — I didn’t draft him anywhere in the first place.

Michael Lorenzen: He’s been pretty blah so far after a hot season opener. I’m not a big strength of schedule guy, but Lorenzen’s good starts recently came against the injury-decimated White Sox and the Athletics (who are just decimated regardless of injuries). He’s a fine DFS play for those “on” days but don’t come yelling at me if you start him in a daily league and he has an “off” day.

Hunter Greene: Making me work overtime, eh? This blurb’s coming in well after I submitted the rest, so I suppose I shoulda made more no-hitter jokes. Wait — the joke is that Greene threw a no-hitter and lost! His strike ratio was way off and he allowed 5 walks. Dude was up near 120 pitches, which we all know is certain arm death in the current world of pitchers. Whateva. Totally Reds thing to do to lose to the [checks notes] Pirates while no-hitting them. Meanwhile, JKJ’s gonna update you on David Bednar’s value as an RP after he notches a [checks notes] save for his no-hit team. What is this — the darkest Moneyball timeline where every player is Youk? Before you get really scared, the box below was generated based on data up through Saturday and does not include Greene’s Sunday gem. So, when DFS players say that they need contrarian pitchers to win the big cash out — this is what they mean:

The Rankings

A horizontal, visual interpretation of the top pitchers, featuring: Shane! Thanks, Google Sheets, real helpful there. It’s McClanahan, bee-tee-dubya.

Always do a sanity check. I’m providing free data that quite literally has a better or higher correlation to fantasy efficacy than many sites that charge an arm and a leg and don’t answer your questions. That said, I’m human and make mistakes; I also submit the article on Sunday so there’s a bit of lag. The source of this data is none other than Razzball itself. So, if you are winning money or having a great time reading the articles, please consider a subscription to the site or drop me a line in the comments and let me know I’m not wasting my life away for naught.

Here’s how to use the list:

• Tier: 1=best, 2=everybody else for 12 team consideration, 3=deep league/dynasty/best ball/tournaments/DFS.
• Name: Player name
• Confidence: The overall score my system outputs. The higher the score, the more confident I am in using the player in the near term. As always, we’re in small sample size territory, so this ranking will get better as the season goes on.
• Own%: This is the rostership % of the player in Razzball Commenter Leagues, run on Fantrax. This % may vary depending on site and format for readers.
• L30$/G: This is how valuable the player has been over the past month. Players with high confidence who have low or negative $/G are “buy low” candidates.  Spot starters/Roleless Robs will have a lower $/G because they play in more games.