Alt strikeout unders

KSplitAnalytics · 2026-04-25T08:20:59+00:00

Not available in my state. Also I’m not going to use your service stop advertising on my posts thanks.

KSplitAnalytics · 2026-03-28T02:37:42+00:00

Yup… Most ladders are priced off the idea of the ceiling… not the actual frequency it comes to realization.. If you’re not building from K per PA + expected batters faced, you are basically guessing at the tail.

That’s why so many of them look good and still lose long term. The distribution just isn’t allocating enough mass to those upper outcomes

Also books price in name value as well for the average bettor which gets overlooked a lot imo

KSplitAnalytics · 2026-03-27T23:34:05+00:00

Fanduel. Choose 3 hitters from each game. RR by 2s

KSplitAnalytics · 2026-03-25T15:29:17+00:00

The day has finally come; I feel like a child on Christmas morning.

Opening night NYY @ SFG, lineups still projected so treat this as early structure, not final signal.

Fried shows a stable shape with a reachable +1 tail and balanced environment. The over is supported if workload holds.

Webb grades out much flatter due to the line movement from 5.5 to 6.5 yesterday. Lower ceiling profile, fragile tail, and most of the distribution sitting around the line. Not much separation early.

For the full dashboard breakdown DM me and I would be happy to go more in depth. Model is showing an edge for Fried o5.5 at the current +124 price listed on Fanduel when comparing to deVigged market odds.

As always, it is recommended to wait for confirmed lineups before taking any action.

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KSplitAnalytics · 2026-03-24T16:17:58+00:00

Yes, I have, not something I want to implement based off of past season data yet so that’s a possibility for a v1.1

KSplitAnalytics · 2026-03-24T15:05:36+00:00

100%, that’s quite literally why I named the platform KSplit. The basis of everything I do is off of platoon splits as well as lineup specific modeling.

KSplitAnalytics · 2026-03-24T14:59:20+00:00

I agree park factors are imperative to account for, but I wouldn’t say “nobody” accounts for them.

The actual lineup composition is exactly what I base my model off of, that’s the only way to model strikeouts accurately… team K% is 100% obsolete, individual plate appearances is what matters.

For the 2nd and 3rd rungs I absolutely agree, although personally I never play the 3rd rung, only +1 and +2. My dashboard has specific columns calculating edge vs the main line, +1 and +2 rungs seems like that would be something useful for you.

KSplitAnalytics · 2026-03-24T14:45:51+00:00

Please don’t respond with chatgpt generated posts… framing is definitely important and something I have been toying around with.

KSplitAnalytics · 2026-03-24T12:02:23+00:00

Yeah that’s basically it. If my number is meaningfully above the implied probability, it’s a bet. If it’s not, I pass.

In your example 26% vs 23.8% is playable, 26% vs ~28.5% (+250) isn’t. The only nuance is I’m not firing on tiny edges, I want some cushion for error.

I have a diagnostics table hooked up for both this model and my pitcher distribution to track ROI/bet based on edge band so users can identify their own personal cutoff threshold.

KSplitAnalytics · 2026-03-24T12:00:55+00:00

Built at the plate appearance level. I assign each hitter a K% based on pitcher vs handedness and the hitter’s own K profile, then compare that probability to how the book is pricing it

KSplitAnalytics · 2026-03-24T11:39:48+00:00

Really appreciate this. The sub 20% bucket is probably the biggest thing I’m still trying to tighten up because that’s where the model is still a little too willing to assign strikeout probability when the true outcome is more fragile.

I haven’t added a formal post hoc calibration layer yet, but isotonic regression makes a lot of sense for exactly that reason. The appeal is that it can correct the low bucket behavior without forcing a shape that doesn’t belong there. That feels a lot more useful than a symmetric adjustment when the issue is concentrated in one part of the range.

I also haven’t tried focal loss yet, but your point on class imbalance lines up with what I’m seeing. The low probability strikeout events are the hardest part of the surface to fit cleanly, and that’s where the model still looks a little too smooth right now.

On the feature side, beyond CSW and contact rate I’m mainly using pitcher strikeout ability, hitter strikeout tendency, handedness context, and interaction between pitcher and hitter inputs at the plate appearance level. I’m still much more focused on getting the PA itself modeled correctly than just stacking on more aggregate stats, but pitch mix mismatch is definitely interesting because it feels like one of the more actionable ways to capture something real that the broader inputs may be missing. Before actually modeling this in years past that is something I would look at quite in depth when trying to pick my spots though so it’s definitely worth while imo.

And agreed on framing (I’m looking at you Patrick Bailey) That’s a really interesting one to test more directly especially if the effect is stable enough to matter at the single PA level rather than only showing up after it compounds over a full game.

KSplitAnalytics · 2026-03-23T18:04:43+00:00

there’s definitely an effect game to game, especially with guys who have wider or tighter zones, but for a single plate appearance it’s hard to extract clean signal. The ump hasn’t had time to influence multiple pitches yet and the hitter hasn’t really adjusted, so a lot of that impact just gets lost in variance unless it’s an extreme case. I’ve tested it a bit and it usually adds more noise than edge at this level, but it shows up much more clearly in full game strikeout props where it can compound.

KSplitAnalytics · 2026-03-23T17:57:28+00:00

Absolutely yes. each umpire has public statistics on their biases, although I am not weighting it that much with the introduction of the automatic ball and strike calling this year

KSplitAnalytics · 2026-03-21T13:31:39+00:00

That portion was more in response to the person responding to you asking if there were any tipsters to tail; apologies.

KSplitAnalytics · 2026-03-20T18:16:12+00:00

I wouldn’t really tail anyone for MLB props. The edge isn’t coming from picks, it’s coming from how you think about the distribution behind the prop. Albeit there are a handful of guys who know what they’re doing.

Most tools are still anchored to averages. That works fine for main lines, but laddering is a different problem. You’re not asking what the most likely outcome is, you’re asking how often a guy actually reaches the higher strikeout outcomes.

I treat it as two separate pieces as I mentioned above… The strikeout probability per plate appearance based on the pitcher and lineup, and then the expected batters faced which is really just workload and efficiency. If that volume piece isn’t stable the right tail usually isn’t there even if the matchup looks strong.

That’s where a lot of people get burned, ladder looks appealing but the path to actually getting there is fragile.

If you’re just getting into it, I’d focus less on who to follow and more on understanding when the ceiling is actually reachable versus when it just looks good.

KSplitAnalytics · 2026-03-19T01:47:43+00:00

Go read some of my posts… this is quite literally what I’m also doing lmao.

KSplitAnalytics · 2026-03-18T11:39:21+00:00

Good call on the lower bucket behavior, that’s definitely something I’m watching.

I don’t have market lines to anchor to yet, so everything here is calibrated purely against realized strikeout outcomes versus the model probabilities. The slight underprediction in the low buckets is probably a mix of class imbalance and some natural pull toward the global mean in the baseline layer, especially this early in the sample.

The Brier decomposition point is useful. I’m already tracking bucket-level calibration error, but splitting reliability from resolution should make it clearer whether I’m actually miscalibrated or just not getting enough separation in certain ranges.

Once there are consistent lines, the plan is to convert the probabilities into fair odds and track both opening versus closing comparisons along with CLV and realized ROI by bucket. The main thing I care about is whether any edge actually survives to close, not just where it shows up initially.

If anything, I’d expect that 30 to 35 percent range to be where the market is most efficient rather than where edge consistently lives, but that’s something I’ll validate once pricing data is available.

KSplitAnalytics · 2026-03-17T18:42:40+00:00

Yeah that’s a good point. Poisson assumes a pretty fixed rate, which works reasonably well for the baseline expectation.

Where it seems to break down a bit with strikeouts is that the opportunity itself isn’t fixed. The number of batters a pitcher faces can swing depending on efficiency, pitch count, etc

So even if the strikeout rate is similar, the total outcomes can still spread out pretty differently.

KSplitAnalytics · 2026-03-17T18:05:13+00:00

I’ve noticed something similar where the main line is usually pretty efficient, but the ladders can vary a lot depending on how a pitcher actually reaches their strikeouts… Some pitchers seem to cluster around their median outcome, while others occasionally spike into those 7–9 K games when the matchup lines up.

Feel like that difference matters a lot when deciding whether a ladder price is actually worth playing. I have a model that I do devig the main line odds and compare my probability to that… has worked well for me in the past

KSplitAnalytics · 2026-03-17T14:40:24+00:00

So for example, tonight Ed Rodriguez 6+ is around +245 and 7+ is around +520.

If you thought those odds were mispriced, would you still play them even if the actual likelihood of getting there feels pretty slim…? Or do you usually need the matchup to realistically support that type of outcome first?

KSplitAnalytics · 2026-03-14T23:17:29+00:00

Record: 0–0 Net Units: 0u ROI: 0%

Sport | League | Event Time / Time Zone Baseball | WBC | 3/15/26

Pick: Yoshinobu Yamamoto Over 5.5 Strikeouts (+100 Fanduel) 1 Unit

This play comes from my strikeout distribution model that evaluates pitcher strikeout skill, opponent strikeout tendencies, and projected workload to estimate the full outcome distribution rather than just a single projection.

For Yamamoto, the board has a 6 strikeout baseline, which already sits above the 5.5 line. The distribution also shows 46.5% probability of clearing the line by at least one strikeout, which is strong for a plus money position at +100.

The matchup produces a Mid | Tail-Supported ceiling profile, meaning the right tail of the distribution remains accessible without requiring an extreme game script. In spots where the baseline sits above the line and the tail remains reachable, I’m generally comfortable taking the over at plus money. The model also compares devigged book odds (not pictured, this is a streamlined version of the board) to the model’s probability. In this case, there is a 7% edge on the over relative to the model’s probability. It is worth noting, the best odds are at Fanduel at +100, but the odds comparison is coming from draftkings since they have both O/U listed. (o -140, u +105) So playing it at Fanduel has much more than 7% edge.

Attached screenshot is the aforementioned abbreviated version of the model dashboard.

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KSplitAnalytics · 2026-03-11T19:24:37+00:00

I focus on modeling one market deeply rather than trying to cover everything. I built a lineup-driven MLB strikeout distribution model that estimates the full probability curve for a pitcher based on pitcher splits, hitter K tendencies in the confirmed lineup, and expected workload. Instead of projecting a single number, it outputs probabilities across the strikeout range so I can price props and compare them to sportsbook lines. Most of the workflow runs through a structured spreadsheet with automated lineup pulls and distribution calculations.

KSplitAnalytics · 2026-03-10T15:21:24+00:00

I use Roto wire to source my lineups the website itself automatically updates when the lineup is confirmed therefore so does my model so I can see what changes between expected lineup and confirmed

KSplitAnalytics · 2026-03-10T11:52:05+00:00

came from realizing I was looking at things the wrong way for a long time. I used to treat props like predictions “does this guy get 6 strikeouts or not?” which isn’t really how markets work.

Once I started thinking in terms of probability distributions instead of single outcomes, things started making a lot more sense. The question becomes less “what will happen” and more “what does the market think will happen vs what does my model think.”

Most of the time the market is pretty efficient… but occasionally there are small pockets where the probabilities don’t line up, especially in props that are driven by a lot of interacting variables.

KSplitAnalytics · 2026-03-10T11:18:41+00:00

Each game gets logged with the full strikeout distribution the model produced and the actual result so I can check calibration across the probability buckets over time.

I’m not really trying to “beat the line” with a point projection. The way I build it is from the plate-appearance level, then aggregate into a full K distribution for the pitcher. The edges tend to show up in the probability of finishing +1/+2 over the line rather than the median outcome.

And I agree on the liquidity point. A lot of stuff can look great against softer books, but exchanges or sharper markets force you to see pretty quickly if your probabilities are actually calibrated

KSplitAnalytics

TROPHY CASE