Friday, February 20, 2026

Hippos Handicapping Panel - Ladbrokes Trophy Handicap Chase Preview

WCMI Hippos Handicapping Panel - Ladbrokes Trophy Handicap Chase Preview

The Hippos Handicapping Panel — where memory and mechanisms collide, but only the horses decide.

Our ongoing exploration of the role of Large Language Models (LLM) in sports trading.

Welcome to the Hippos Handicapping Panel — a virtual round‑table of racing minds brought to life with the help of an LLM. Each Hippo has a distinct voice:

  1. Mick – Aussie handicapper and professional punter
  2. Pearl – Canadian academic and causal analyst
  3. Philip – British host who keeps them honest and sneaks in his own Weekend Warrior longshots

Together they blend events and explanations into a lively debate that is equal parts analysis and paralysis.

Art vs Science of Picking Winners

Ladbrokes Trophy Handicap Chase Preview

1) Race context and likely shape

Kempton’s three-mile chase on Good To Soft is a slightly different “staying” test to the attritional mud-baths: it’s right-handed, relatively flat, rhythm-heavy, and it rewards the horse who can hold a position, jump economically, and then actually quicken off the final bend rather than merely out-stay rivals in slow motion. Over 3m here, you’re still buying stamina, but you’re also buying cruising speed under pressure—and that’s why these Kempton handicaps can look like proper puzzles even with only 13 runners.

The field composition is tidy and concentrated: multiple bullets from the same arsenals. Dan Skelton doubles up with Boombawn and Hoe Joly Smoke, Ben Pauling brings Henry’s Friend and Leader In The Park, Emmet Mullins rolls in with Chance Another One and the relentlessly progressive Rising Dust, while Anthony Honeyball fields Gustavian and the younger Kdeux Saint Fray. There’s no ballot drama in what we’ve got here—this is the actual 13, end of.

The market scaffold is telling you it’s open without being anarchic: Katate Dori (6/1) sits atop the pecking order, then a tight clutch around The Doyen Chief (15/2), Hoe Joly Smoke (7/1) and Kdeux Saint Fray (7/1), with plenty of plausible stories just behind. And on “weight-of-money”: I can’t see a live exchange screen from here, so we’ll treat the fixed-odds shape as a proxy—but in these races, when the crowd really decides, it often happens late, and it often happens brutally.

2) Philip (Host) — opens the panel

Philip: Welcome back to Kempton, where the fences come at you like deadlines and your “good thing” can turn into an apology by the second circuit. Mick, I’ll start with the old Socratic nuisance-question: what pattern have you seen before that helps you sort this 3m handicap chase on Good To Soft—who fits the Kempton riddle?

3) Mick (Memory Lane) — case-based reasoning, market feel, and picks

Mick: Yeah righto, Phil—Kempton three-mile chases, I file ’em under “stayers who can travel.” It’s not just lungs; it’s rhythm. Horses that spend the first two miles fighting the jockey, or clouting one every now and then, they don’t magically become efficient late.

First thing I do is the stable-temperature check. Skelton’s outfit is ticking along (and you’ve got Boombawn and Hoe Joly Smoke in the same colours of competence), Pauling’s numbers look hot on that RTF% read, Mullins is Mullins—when he sends one, it’s rarely for a picnic—and Honeyball tends to place one to nick a pot when the profile fits. That’s not “trainer worship”, mate, it’s just acknowledging the humans are part of the machine.

Then I go to the public intel loop. I’ll skim the Racing Post racecard for this exact contest. I’ll check Pricewise as a temperature gauge rather than gospel, and when I’m doing the “pundits’ pool” thing I literally punch names into the search box—Kevin Blake, Johnny Dineen, and Ruby Walsh. It doesn’t mean they’re right—means I know what the herd is thinking before I decide whether to fade it.

Now the Fermi bit—pub-maths, not physics. In a 13-runner handicap chase, I mentally allocate something like: “How many can genuinely win if they jump round?” Maybe five or six. Then I ask: which of those are priced like they can win, and which are priced like they can merely talk like they can win.

So, my plays.

Win/main pick: Katate Dori at 6/1. The profile says “proper engine with enough pace for Kempton,” and that matters here. You’re not asking for a plodder, you’re asking for a traveller who can still see his fences when the tempo lifts. Off 10st 13lb as well, you’re not giving away sacks of weight.

Safety each-way: Lookaway at 11/1 for the place. The recent sequence reads like a horse who’s found his lane, and the Kempton test often rewards the ones who can hold a position and keep doing the same thing, correctly, again and again.

Value swing: Henry’s Friend at 14/1 looks overpriced to me. Pauling’s yard being in-form doesn’t win the race on its own, but it does mean I’m less scared of the “will he run to himself?” question. If he runs to the number he can run to, 14s is a big enough parachute.

And mate, I’ll leave you with my favourite gambling truth: you don’t need to be a genius—just be less wrong than the market for long enough.

4) Philip to Pearl — probing transition

Philip: Pearl, Mick’s basically saying, “I’ve seen this movie: Kempton wants a traveller, and the crowd leaves clues.” But isn’t that exactly how people end up paying for familiarity rather than causes? If we took Mick’s casebook away, what causal story would you write for who wins this?

5) Pearl (Meaningful Musings) — causal model, counterfactuals, and picks

Pearl: Let’s build a simple causal map, because these handicaps punish us when we confuse correlation with mechanism.

A minimal DAG for this race looks like:

Going → Pace/Tempo → Jumping Pressure → Errors/Lost Ground → Finishing Kick → Result
Weight → Fatigue → Jumping Pressure → Result
Trainer form → Fitness/Readiness → Jumping & Stamina → Result

The key point is that running position is often a mediator here: good travellers secure a position more easily, and that position reduces the number of “panic jumps” taken off a wrong stride. But position is also influenced by early pace and individual speed, so if we condition too heavily on “was handy last time,” we risk a collider-type mistake—crediting position when the real driver was a combination of pace context and horse ability.

Now the counterfactuals.

If the early tempo is only even, Kempton can turn into a “who can quicken late” contest; if the early tempo is honest, it becomes “who stays while still jumping.” On Good To Soft, I’m expecting something in the middle: enough pressure that inefficient jumpers are taxed, not so much that it’s a war of pure attrition.

That makes me interested in horses where the pathway “efficient jumping + manageable weight + repeatable effort” is most plausible.

Win/main: The Doyen Chief at 15/2. The causal story I like is: workable weight (11st 2lb), a profile that suggests he can reproduce his effort, and a style that should allow him to stay in touch without spending energy early. In this race, conserving energy causes a better finish because the final mile at Kempton is still run at a meaningful speed.

Each-way structural: Leader In The Park at 14/1 offers structural value because the pathway to a place is clearer than the pathway to a win for many in here: he doesn’t need to be the best horse, he needs to be among the few who (a) jump adequately, (b) don’t get detached, and (c) are still functional turning in.

Progressive risk: Kdeux Saint Fray at 7/1 for those seeking upside. At 6yo he has the “improvement” lever—handicaps are often decided by who moves forward rather than who repeats. If he improves even a small amount, the downstream effect is disproportionate: a half-length saved at two fences can become two lengths at the line when the group is compressing late.

And I’ll repeat the mantra: prediction is not explanation—but explanation helps you choose which predictions to trust.

6) Philip challenges Mick — pressure-testing the casebook

Philip: Mick, you’ve put Katate Dori at 6/1 on top as the “Kempton traveller,” but that’s also the crowd’s first instinct. Where’s the edge if you’re agreeing with the scaffold? And on Henry’s Friend at 14/1—are you buying a revival, or just buying the price and hoping?

7) Mick rebuttal — practical punter vs theory

Mick: Fair crack, Phil. But “favourite” doesn’t mean “no edge,” it means the market’s conceded he’s a player. The edge comes when the favourite is still underestimating a key thing—like track fit. Kempton’s not random; it’s a specific exam. If I reckon Katate Dori at 6/1 is the right type for the exam, I don’t need him to be a secret, I need him to be a touch better suited than the price implies.

And Henry’s Friend at 14/1—yeah, I’m buying the price. That’s the job. I’m not saying he’s the likeliest winner; I’m saying the market might be overstating the downside. In a handicap chase, “variance” is a feature, not a bug. I’ll cop the losing runs when the overs are there.

8) Philip challenges Pearl — pressure-testing causality

Philip: Pearl, your model loves clean pathways—efficient jumping, manageable weight, repeatable effort. But The Doyen Chief at 15/2 has a “P” in the recent form line. Isn’t that precisely the kind of brittle signal where a causal story can become a comforting story?

9) Pearl rebuttal — defending the framework

Pearl: It’s a fair critique, and it’s why causal modelling doesn’t ignore noise—it tries to locate it.

That “P” is an outcome, but not the cause. The question is whether that failure is persistent (a latent issue like chronic jumping inefficiency or physical limitation), or situational (pace, an error cascade, being asked at the wrong time). My framework says: don’t treat it as a single, monolithic trait. If the mechanisms that predict performance today—weight carried, likely tempo, and the ability to conserve energy—are aligned, then The Doyen Chief at 15/2 can still be a rational selection even with an adverse datapoint. We’re not excusing the risk; we’re pricing it.

10) Philip’s Summary — synthesis, disagreements, and my picks

Philip: Right, let’s distil the philosophy into something you can actually bet without needing a postgraduate seminar or an Australian accent.

Mick’s leaning into the “Kempton traveller” thesis and basically says: in these compressed handicaps, a horse who travels and jumps is half the battle; he’s happy to side with the market when the type is right, hence Katate Dori at 6/1, and he’s hunting price insurance with Henry’s Friend at 14/1. Pearl is building a cleaner causal ladder—weight to fatigue, tempo to jumping pressure, efficiency to finishing kick—and she’s landing on The Doyen Chief at 15/2 as the most plausible “do the basics, then finish” candidate, with structural each-way logic around Leader In The Park at 14/1, and upside on the younger Kdeux Saint Fray at 7/1.

Where do they converge? They’re both, in different languages, warning you that Kempton is not simply “three miles = stamina.” It’s stamina with pace and precision. Where do they diverge? Mick trusts the lived pattern and the price; Pearl trusts the mechanism and the pathway.

My consolidated plays, trying to be neither precisely wrong nor poetically vague:

Win/main: The Doyen Chief at 15/2 — the profile reads like a horse who can get into a rhythm, stay in touch, and still have something left when others are merely surviving.

Each-way backup: Lookaway at 11/1 — the recent form and the “repeatable effort” angle make sense in a race where finishing positions often go to the horses who simply keep doing their job.

Risk add: Katate Dori at 6/1 — yes, it’s near the top of the market, but if Kempton turns into a travelling-and-kicking contest rather than a grind, I can see why he’s there.

And as the old racing line goes: the horse doesn’t know his price, but the punter must.

11) Weekend Warrior — outsider (20/1+), Philip’s narrative longshot

Philip: Now, for my weekly act of romantic self-sabotage: the Weekend Warrior longshot.

I’m going with Gustavian at 28/1.

He’s the veteran in a field that’s largely hunting “progress,” and that’s precisely why the story appeals: Kempton sometimes rewards the old pro who’s seen enough fences to stop arguing with them. If the pace collapses into errors—if younger legs turn into younger mistakes—then the one thing experience can still buy you is a clear round and a late nibble at the places.

Is he in the model? Barely. Is he in the market? Not really. Is he in my heart for bragging rights? Unfortunately, yes. And if he lands a place, I’ll be unbearable until at least Tuesday—possibly Wednesday, depending on how many people answer my texts.

12) Quick racecard crib

  • Race: Ladbrokes Trophy Handicap Chase
  • Course: Kempton
  • Time/Date: 15:35, 2026-02-21
  • Distance: 3m
  • Going: Good To Soft
  • Runners: 13
  • Winner’s prize: £85,425

13) Guide odds (current prices as provided)

Horse Odds
Katate Dori 6/1
Hoe Joly Smoke 7/1
Kdeux Saint Fray 7/1
The Doyen Chief 15/2
Deep Cave 10/1
Chance Another One 10/1
Lookaway 11/1
Soul Icon 12/1
Rising Dust 12/1
Henry's Friend 14/1
Leader In The Park 14/1
Boombawn 18/1
Gustavian 28/1

14) Web Sites (Alphabetical)

Generated by Hippos Handicapping Preview Panel - Poe API v1.00.00 [ https://vendire-ludorum.blogspot.com/ ]

Posted by at
Labels: , ,

Friday, February 06, 2026

TMDA - Profitable, Sustainable, and Survivable

WCMI TMDA - Profitable, Sustainable, Survivable

Beyond Expected Value and Likely Profit: Adding Risk of Ruin

In a previous post, we introduced the Dual-Metric Decision Algorithm (DMDA), which combined Expected Value (EV) and Likely Profit (LP) to provide a more comprehensive framework for evaluating betting decisions. While this approach was an improvement over solely relying on EV, it still lacked a critical component: explicit risk management.

Today, we are extending that framework with the Tri-Metric Decision Algorithm (TMDA), which adds a third dimension: Risk of Ruin (RoR). This addition addresses a fundamental question that every bettor must answer: "What is the probability that my bankroll will decline to an unacceptable level within a given time horizon?"

TMDA Tri-Metric Decision Algorithm Illustration

Missing Piece: Risk of Ruin

Expected Value tells you if a bet is statistically profitable. Likely Profit tells you if it is sustainable in terms of geometric growth. But neither metric explicitly addresses the volatility risk — the chance that short-term fluctuations will devastate your bankroll before the long-term edge materializes.

Consider this: you might have a positive EV bet with decent LP, but if your stake size is too aggressive relative to the volatility of outcomes, you could hit your drawdown threshold long before experiencing the expected growth. This is where Risk of Ruin (RoR) becomes essential.

Key Insight: RoR quantifies the probability of your bankroll falling below a critical threshold (e.g., 50% drawdown) within a specified number of bets. It is not just about whether you'll win in the long run — it is about whether you'll survive to reach the long run.

Building the Foundation

Let's start with our familiar canonical example and build up to the full TMDA framework:

Parameter Value
Initial Bankroll (B) $10,000
Decimal Odds (O) 1.9091
Win Probability (P) 55.00%
Stake Fraction (F) Variable
Drawdown Threshold 50%
Time Horizon 2,300 bets

Win-Balance and Loss-Balance Multipliers

As before, we calculate the bankroll state after wins and losses:

WB = 1 + (F × (O - 1))
LB = (1 - F)

Where WB represents the bankroll multiplier after a win, and LB represents the bankroll multiplier after a loss.

Expected Value and Likely Profit

The first two metrics remain unchanged from DMDA:

EV = (WB × P) + (LB × (1-P)) - 1
LP = (WB^P × LB^(1-P)) - 1

EV represents the arithmetic mean return per bet, while LP represents the geometric mean return, which accounts for compounding effects.

Log-Drift and Log-Volatility

To calculate Risk of Ruin, we need two additional statistics derived from the log-space representation of bankroll growth:

μ = P·ln(WB) + (1-P)·ln(LB)
σ² = P·(ln(WB)-μ)² + (1-P)·(ln(LB)-μ)²

Here, μ (mu) represents the expected log-growth per bet (drift), and σ (sigma) represents the standard deviation of log-returns (volatility). These metrics transform the problem into a continuous-time random walk, which allows us to apply diffusion approximations.

Technical Note: LP and μ are closely related but not identical. Since LP = exp(μ) − 1, they differ by higher-order terms. For small values (as in typical betting scenarios), LP ≈ μ to several decimal places, which is why the worked example shows them as equal when rounded to 0.000819.

Risk of Ruin Formula

Using the reflection principle from probability theory, we can approximate the probability of hitting a drawdown threshold d within n bets:

RoR ≈ Φ((-d - μ·n)/(σ·√n)) + exp((-2·μ·d)/σ²)·Φ((-d + μ·n)/(σ·√n))

Where Φ is the cumulative distribution function of the standard normal distribution, and d = -ln(drawdown_fraction). For a 50% drawdown threshold, d = 0.693.

Note: This formula assumes continuous betting and uses the normal approximation of the underlying diffusion process. For small sample sizes or extreme probabilities, the approximation may be less accurate, but it provides an excellent practical guideline for typical betting scenarios.

Tri-Metric Decision Algorithm

With all three metrics in hand, we can now construct the TMDA decision framework:

if EV ≤ 0:
    decision = 'Avoid'  # Statistically unprofitable
elif LP ≤ 0:
    decision = 'Reduce stake'  # Geometric decay despite positive EV
elif RoR > tolerance:
    decision = 'Reduce stake'  # Risk exceeds acceptable threshold
else:
    decision = 'Accept'  # All metrics favorable

This hierarchical decision tree ensures that we only accept bets that satisfy all three conditions:

  1. Positive EV — The bet is statistically profitable
  2. Positive LP — The bet exhibits sustainable geometric growth
  3. Acceptable RoR — The risk of significant drawdown is within tolerance

Worked Example

Let's examine our canonical example with a stake fraction of F = 2%:

Step 1: Calculate WB and LB

WB = 1 + (0.02 × (1.9091 - 1)) = 1.018182
LB = 1 - 0.02 = 0.98

Step 2: Calculate EV and LP

EV = (1.018182 × 0.55) + (0.98 × 0.45) - 1 = 0.001000
LP = (1.018182^0.55 × 0.98^0.45) - 1 = 0.000819

Step 3: Calculate μ and σ

μ = 0.55·ln(1.018182) + 0.45·ln(0.98) = 0.000819
σ² = 0.55·(ln(1.018182)-0.000819)² + 0.45·(ln(0.98)-0.000819)² = 0.000362
σ = 0.01901

Step 4: Calculate RoR

For a 50% drawdown over 2,300 bets:

d = -ln(0.5) = 0.693
RoR ≈ 4.15%

Step 5: Apply TMDA

  • EV = 0.001000 ✓ Positive
  • LP = 0.000819 ✓ Positive
  • RoR = 4.15% ✓ Below 5% tolerance

Decision: Accept — The bet has positive EV, positive LP, and the risk of experiencing a 50% drawdown within 2,300 bets is within our 5% tolerance threshold.

Finding the Optimal Stake

One powerful application of TMDA is determining the maximum stake size that keeps RoR within acceptable bounds. Using binary search or numerical optimization, we can find the stake fraction F* that satisfies:

RoR(F*) = tolerance

The dashboard computes this optimal stake numerically using the findStakeForTargetRoR() function. For any given parameters, the "Optimal Stake for Target RoR" section in the dashboard will display the maximum stake fraction that meets your risk tolerance, along with the corresponding dollar amount for your bankroll.

Interactive Dashboard

To explore TMDA across different parameters, we have created an interactive dashboard where you can:

  • Adjust odds, probabilities, and bankroll amounts
  • Compare multiple stake fractions simultaneously
  • Visualize the relationship between EV, LP, and RoR
  • Find the optimal stake for your risk tolerance


(Opens in a new window; allow popups if prompted)

Conclusion

The Tri-Metric Decision Algorithm represents a significant evolution beyond traditional EV-only approaches. By incorporating Likely Profit, we account for geometric compounding effects. By adding Risk of Ruin, we explicitly manage volatility risk and ensure that our stake sizing aligns with our risk tolerance.

However, TMDA is not a silver bullet. It assumes:

  • Independent, identically distributed bets
  • Accurate probability estimates
  • Continuous betting (for the RoR approximation)
  • Fixed odds and probabilities across all bets

Real-world betting involves correlated outcomes, model uncertainty, and dynamic market conditions. TMDA should be viewed as a framework for thinking rather than a mechanical system. It provides a structured approach to balancing profitability and risk, but successful implementation requires judgment, experience, and continuous refinement.

Bottom Line: While EV tells you if a bet is profitable, and LP tells you if it is sustainable, RoR tells you if it is survivable. TMDA integrates all three perspectives to make more informed betting decisions.

Asymmetric Payoffs and Ruin Theory

The TMDA framework developed above treats every bet as a two-outcome event with a single pair of win/loss multipliers derived from decimal odds. That model works well when the payoff structure is roughly symmetric — for example, a coin-toss-style wager where the amount you can win and the amount you can lose are of similar magnitude. But many real-world risk-taking environments feature asymmetric payoffs: losses are small and frequent, while gains are large but rare.

The Problem with Symmetric Assumptions

Consider venture capital, where most individual investments fail but a single outsized success can return the entire fund. Or options trading, where a strategy of buying out-of-the-money puts involves paying small, regular premiums in exchange for rare but massive payoffs during market crashes. Tail-risk hedging strategies follow the same pattern.

In all these cases, the payoff structure looks like:

Win: +K units with probability p
Loss: −1 unit with probability (1 − p)

where K is the asymmetry ratio — the number of loss-units that a single win recovers. When K = 1 we are back to the symmetric case. When K = 9, a single win recovers nine consecutive losses. When K = 20, a single win recovers twenty.

The question is: does TMDA's diffusion-based Risk of Ruin remain accurate when payoffs are this skewed? Recent work by Whelan (2025) provides the analytical tools to answer this question precisely.

Discrete Markov-Chain Ruin Model

Whelan generalises the classical gambler's ruin problem to asymmetric payoffs. The wealth process is modelled as a discrete Markov chain on states 0, 1, 2, …, T, where the investor starts at wealth n, is ruined at 0, and succeeds at T (or T + 1, …, T + K − 1).

Notation: In Whelan's paper, μ denotes the expected arithmetic profit per round (i.e. E[Xi] = μ). In the TMDA framework above, μ denotes the expected log growth per bet. The two quantities are closely related but not identical — the log-drift is always smaller due to the variance penalty. When citing results from the paper below, μ refers to the paper's arithmetic convention unless otherwise noted.

At each step, wealth moves:

i → i + K    with probability p
i → i − 1    with probability (1 − p)

The probability of reaching each terminal state satisfies a difference equation whose characteristic equation is:

p·rK+1 − r + (1 − p) = 0

This polynomial has K + 1 roots. Using the matrix method of Harper & Ross (2005), the full set of absorption probabilities can be computed exactly via matrix inversion, giving exact ruin probabilities without any diffusion approximation.

Bridging Discrete and Continuous

TMDA works in log-return space with drift μ and volatility σ. The discrete asymmetric game can be mapped into this space by converting step sizes into fractional (multiplicative) returns.

Let the stake fraction be F, the fractional win be a, and the fractional loss be b. Then:

Win multiplier: MW = 1 + F·a
Loss multiplier: ML = 1 − F·b

The per-bet log-returns are:

X = ln(MW) with probability p
X = ln(ML) with probability (1 − p)

And TMDA's log-drift and log-volatility become:

μ = p·ln(MW) + (1−p)·ln(ML)
σ² = p·(ln(MW) − μ)² + (1−p)·(ln(ML) − μ)²

These (μ, σ) values are exactly the inputs TMDA already uses. In the small-step limit (large bankroll, small fractional stakes), the discrete chain converges to TMDA's continuous diffusion — so the two frameworks are mathematically consistent.

Where Diffusion Breaks Down

The critical insight from Whelan's analysis is that the diffusion approximation becomes increasingly inaccurate as payoff asymmetry grows. Specifically, for positive-EV games with high K, the diffusion-based RoR underestimates the true ruin probability — sometimes dramatically.

Consider a concrete example:

ParameterValue
Win probability (p)0.10
Win payoff (a)+9%
Loss payoff (b)−1%
Asymmetry ratio (K = a/b)9
Bankroll (W0)100
Ruin threshold (Wmin)50 (50% drawdown)
Horizon (N)500 bets

Step 1: Compute μ and σ

μ = 0.10·ln(1.09) + 0.90·ln(0.99) = −0.00043
σ ≈ 0.0287

Note the negative log-drift despite a positive arithmetic EV — this is the classic "volatility drag" effect, amplified by asymmetry.

Step 2: Diffusion-based RoR (TMDA)

d = ln(100/50) = 0.693
RoRTMDA ≈ 30.8%

Step 3: Exact Discrete RoR (Monte Carlo simulation)

RoRexact ≈ 41%
Gap: The diffusion approximation underestimates ruin risk by approximately 10 percentage points. The many small losses push the bankroll toward the boundary faster than the continuous model predicts, because the discrete chain captures the true step-by-step dynamics of sequential losses between rare wins.

Unified TMDA

To correct for this, we can blend the diffusion estimate with the exact discrete result using a weighting that reflects the degree of asymmetry:

K = a / b    (asymmetry ratio)
λ = 1 / (1 + K)    (blending weight on diffusion)

RoRunified = λ · RoRdiffusion + (1 − λ) · RoRexact

For our example with K = 9:

λ = 1/10 = 0.10
RoRunified = 0.10 × 0.308 + 0.90 × 0.41 ≈ 0.40

This unified estimate of ~40% matches the exact discrete result almost perfectly, while the standard TMDA diffusion would have reported only ~31%.

Method RoR Notes
Exact discrete ruin 41% Ground truth (Monte Carlo)
Standard TMDA (diffusion) 31% Underestimates risk due to ignoring asymmetry
Unified TMDA 40% Matches exact result; asymmetry-aware

Practical Implications

Whelan's analysis yields several findings that directly inform how TMDA should be applied to asymmetric strategies:

  1. Positive-EV strategies can be destroyed by variance. Even when expected return per play is held constant at μ = +0.01, increasing asymmetry from K = 1 to K = 20 raises the ruin probability from ~13% to ~64% (for an investor staking 1% with initial wealth n = 100, targeting a tripling of wealth). The rare big wins simply do not arrive often enough to prevent early ruin.
  2. Negative-EV strategies can be partially rescued by variance. In the symmetric case with μ = −0.01, ruin is near-certain (~98%). But at K = 20, ruin falls to ~71% and expected wealth recovers to ~90% of the initial amount. The occasional large win can rescue an otherwise losing game.
  3. Stake-size effects diminish as K grows. For near-symmetric games (K ≈ 1), stake size has a huge impact on outcomes — consistent with the Kelly criterion. But for highly asymmetric games (K ≥ 20), changing the stake fraction makes relatively little difference, because variance is dominated by the payoff structure itself rather than position sizing.
  4. The Kelly criterion connects naturally. For an asymmetric game with expected return μ and winning profit K, the Kelly-optimal stake fraction is approximately μ/K. When TMDA is applied with stakes above this level, the model correctly flags elevated ruin risk — but only if the asymmetry is accounted for via the unified RoR.
Looking Ahead: In a forthcoming update, we may extend the interactive TMDA dashboard to support asymmetric payoff structures directly. The updated dashboard will compute diffusion-based, exact discrete, and unified RoR side by side — letting you see exactly how payoff asymmetry affects survivability for any strategy. It will also include a stake-grid visualisation showing where the three methods diverge.

References

  • Feller, W. (1950). An Introduction to Probability Theory and Its Applications, Wiley.
  • Harper, J.D. and K.A. Ross (2005). "Stopping Strategies and The Gambler's Ruin," Mathematics Magazine, 78, 255–268.
  • Kelly, J.L. (1956). "A New Interpretation of Information Rate," Bell System Technical Journal, 35, 917–926.
  • Whelan, K. (2025). "Ruin Probabilities for Strategies with Asymmetric Risk," University College Dublin. [PDF]
Posted by at
Labels: , , , ,
Hippos Handicapping Panel - William Hill Hurdle Preview

WCMI Hippos Handicapping Panel - William Hill Hurdle Preview

The Hippos Handicapping Panel — where memory and mechanisms collide, but only the horses decide.

Our ongoing exploration of the role of Large Language Models (LLM) in sports trading.

Welcome to the Hippos Handicapping Panel — a virtual round‑table of racing minds brought to life with the help of an LLM. Each Hippo has a distinct voice:

  1. Mick – Aussie handicapper and professional punter
  2. Pearl – Canadian academic and causal analyst
  3. Philip – British host who keeps them honest and sneaks in his own Weekend Warrior longshots

Together they blend events and explanations into a lively debate that is equal parts analysis and paralysis.

Art Vs Science Of Picking Winner

William Hill Hurdle Preview

1) Race context and likely shape

Newbury’s William Hill Hurdle is “two miles” only on the card; at 2m 69y on Heavy ground it behaves more like an attritional test where rhythm, balance, and the ability to hold form through the final furlong up that long home straight matter as much as raw speed. It’s a big, galloping hurdles track: if you’re even slightly inefficient at the obstacles, Heavy ground turns that into a compounding tax rather than a one-off mistake.

The field is at the declared maximum, 16 runners, so there’s no ballot story to tell—everyone who mattered has made it in—which usually means a genuine handicap puzzle rather than a “who got lucky with the cut” scenario. The market scaffolding is clear enough: Let It Rain at 5/2 is the focal point, with Un Sens A La Vie at 6/1, All In You at 7/1, Tutti Quanti at 15/2, and Lanesborough at 8/1 forming that second tier where punters typically try to “beat the jolly” without diving straight into the fog.

And on the “crowd wisdom” signals: we don’t have a live Betfair Weight-of-Money ladder in front of us today, so we can’t pretend we’ve watched late steamers appear; but the shape of the prices still tells you what the crowd thinks is most robust on Heavy ground—progressive profiles and perceived leniency in the handicap tend to get supported, while anything that looks like “needs good ground / needs a break / needs everything to fall right” gets pushed out, even if the raw ratings say otherwise.

2) Philip (Host)

Philip: Welcome back to the Hippos panel—Newbury, Heavy ground, and a handicap that will punish optimism. Mick, you’ve been around enough rucks at the bookies to know when a market is being clever and when it’s just being fashionable. Is Let It Rain at 5/2 a proper anchor here, or is that the sort of favourite that looks solid right up until the last hurdle?

3) Mick (Memory Lane)

Mick: Righto, mate—this is the sort of race where blokes tell ya “it’s a lottery” and then back the favourite anyway. I’ll start with the obvious: on Heavy ground at Newbury, I want a horse who can keep rolling when the others go from gallop to grind. You can dress it up as “sectionals” and “efficiency” but in the pub it’s just: who’s still travelling three out?

Now, stable vibes and all that—people love it, I love it. You’ve got Dan Skelton with a pair, and when the Skeltons point one at a big Saturday handicap you don’t ignore it. Paul Nicholls being RTF 70% is the sort of number that makes the algorithm crowd purr, and I get it: a Nicholls horse like Tutti Quanti has that “does the job” profile. But the market’s telling you what it thinks the plot is, and it’s centred on Let It Rain at 5/2 carrying only 11st 0lb off OR 124—that’s the handicapper saying “prove it,” and the punters saying “we think he will.”

Collateral form and Fermi-estimates—here’s my napkin math: on Heavy ground, I mentally add a “slog premium” that turns a clean two-mile horse into a doubtful stayer if they don’t relax. I’m not saying it’s exactly +3 seconds per mile or whatever—nobody knows—but roughly speaking you need an extra gear of stamina, and a lot of these are priced as if it’s a neat little two-miler on Soft. That’s where mistakes happen.

I’ve also been doomscrolling the usual tipster ecosystem—some of it is noise, some of it is helpful framing.

And because we’re talking “wisdom of the crowd,” you can’t ignore where the real-time crowd trades:

…and the price-comparison hive mind:

Selections—no mucking around:

My win/main pick is Let It Rain at 5/2 because the entire profile screams “handicap blot if he’s as effective in the mud as the market assumes,” and the low weight matters when the ground is trying to steal your lungs.

My safety each-way is Un Sens A La Vie at 6/1 for the place because he’s got that “stays the effort” look on paper—TS 133 pops, and 11st 6lb is workable if this turns into a stamina test rather than a sprint.

My value swing is Wreckless Eric at 25/1—mate, the TS 141 is enormous in this context and the RPR 142 says he can run a race way better than the price implies. If he’s ever going to make the market look silly, it’s in a race where half the field are going to cry enough from the last.

And I’ll leave you with the old punter’s prayer: I’d rather be approximately right than precisely broke.

4) Philip to Pearl

Philip: Pearl, Mick’s basically saying, “trust the market on the favourite, then buy yourself some insurance with a place angle, and take one big swing where the raw figures don’t match the price.” But isn’t that mixing three different models—market, ratings, and vibe—without asking what actually causes performance on Heavy ground at Newbury?

5) Pearl (Meaningful Musings)

Pearl: It is mixing models, Philip, but the deeper question is whether the mixture is coherent. On Heavy ground, the causal structure is unusually strong because the surface amplifies a small set of mechanisms.

Here’s a simple verbal DAG for this race: Ground (Heavy ground) increases energy cost, which reduces late-race speed; pace pressure influences in-running position, which mediates jumping accuracy under fatigue; and weight carried affects fatigue, which then affects both jumping and finishing effort. Meanwhile, class/ability is a confounder because it drives both the handicap mark (and therefore weight) and the horse’s baseline performance. If we condition too heavily on the market price, we risk a collider problem: price is influenced by ability and narrative information (stable reputation, recency bias, “unlucky last time”), and treating price as “truth” can open misleading paths.

So I like to do counterfactual checks. If the early pace is strong—say several riders decide they want a position before the first two flights—then the finishing order will disproportionately reward stamina and efficient hurdling late. If the pace is steadier, the race becomes more about tactical speed and who can quicken off a slow tempo in gluey ground, which is rare but not impossible at Newbury.

Now, selections anchored to mechanism rather than memory:

My win/main is Tutti Quanti at 15/2 because the causal pathway I trust here is “robust current form plus the ability to maintain effort under load.” He’s up at 12st 0lb, yes, but his profile—RPR 140, TS 131, and a strong yard signal (RTF 70%)—suggests the underlying ability can survive the Heavy-ground energy tax if he jumps cleanly.

My each-way structural value is The Hardest Geezer at 18/1 because 10st 12lb is meaningful in a fatigue-mediated race, and the RPR 143 hints at a ceiling that the market may be underweighting. Structurally, low weight is not “nice to have” on Heavy ground; it is a direct causal lever on late-race resilience.

My progressive risk is Bubble Dubi at 22/1 because the upside case is clear: TS 132 and RPR 140 imply capability, and at this price you’re being paid to take uncertainty about how the race environment interacts with him. In causal terms, you’re buying optionality on the “handles the mud and stays engaged” route; if that route is active, the number is too big.

And I’ll say it plainly: prediction is not explanation—but explanation is how you avoid repeating the same mistake at different odds.

6) Philip challenges Mick

Philip: Mick, Pearl’s effectively accusing you—politely—of outsourcing too much to the crowd when you side with Let It Rain at 5/2, and then trying to “buy back” edge with Wreckless Eric at 25/1 on a big Topspeed. Are you just building a betting slip that feels diversified, rather than one that’s logically consistent?

7) Mick rebuttal

Mick: Nah, that’s the academic trap, Philip—thinking punters have to marry one religion. In the real world, you’re paid for being right, not for being pure.

The market part is simple: in handicaps like this, the favourite’s often the horse with the fewest unanswered questions according to the people who bet for a living. That’s not romance, that’s rent money. So Let It Rain at 5/2 is me saying, “I’ll stand with the crowd when the crowd’s probably got information.”

Then Un Sens A La Vie at 6/1 is just practical: on Heavy ground, you want a horse who can keep finding. That’s not a different model, that’s the same model—survivability.

And Wreckless Eric at 25/1 is the classic mispricing play: I’ve seen heaps of races where one horse has a figure that says he belongs, but the market bins him because the recent formline looks ugly. If the TS 141 is even partially real on the day, the price is wrong. You don’t need perfect logic—just a repeatable way to spot when the crowd has overreacted.

8) Philip challenges Pearl

Philip: Pearl, your DAG is tidy—almost too tidy. The problem with causal stories in racing is they can explain anything after the fact. Why should we believe your “weight → fatigue → late jumping → finish” chain will matter more than, say, one bad mistake at the wrong hurdle, or a rider making the wrong mid-race decision?

9) Pearl rebuttal

Pearl: Because the DAG isn’t denying randomness; it’s organising where randomness is most likely to become decisive. A single error is often not exogenous—it’s frequently caused by fatigue, poor position, or pressure. Heavy ground increases fatigue, which increases the probability of errors and the cost of recovering from them. That’s exactly why I foreground weight and resilience.

Also, I’m not claiming determinism. I’m saying: if we have to choose variables to trust, choose ones that remain causally relevant across pace scenarios. Weight and ground sensitivity do that; “tactical luck” is real, but it’s not a lever we can price well.

So yes, a rider can change the outcome. But the reason some horses are more robust to those rider-induced perturbations is that they have more physical margin—less fatigue at the same point in the race—which returns us to the same mechanism.

10) Philip’s Summary

Philip: Here’s what I think we’ve learned, in the way that only a Saturday handicap can teach you—by making clever people disagree with themselves in public.

Mick’s case is that the market is a useful information aggregator, and Let It Rain at 5/2 is the most plausible “least-wrong” answer to a puzzle that will defeat most single-theory approaches. He then tries to capture place-probability with Un Sens A La Vie at 6/1, and he’s waving a big, cheeky flag at Wreckless Eric at 25/1 because the figures shout louder than the price.

Pearl’s counter is more structural: Heavy ground makes fatigue the central mediator, so weight and robustness should be treated as causal levers rather than trivia. That’s how she lands on Tutti Quanti at 15/2 as a credible top-of-handicap type despite the impost, and finds “structural value” in The Hardest Geezer at 18/1, with Bubble Dubi at 22/1 as the upside play if the uncertainty resolves the right way.

Where they converge is actually important: both are implicitly pricing the Heavy ground as a selection device—it doesn’t just slow them down, it separates those who can maintain action and decision-making late. Where they diverge is on how much deference to pay the crowd versus the mechanism.

My own consolidated trio, trying to be honest about both information and causality:

My win/main is Let It Rain at 5/2, because the market has made him the spoke of the wheel and the weight looks a real advantage if this becomes a war of attrition.

My each-way backup is Un Sens A La Vie at 6/1, because he looks like the type who can stay involved when others turn it into survival.

My risk add is Wreckless Eric at 25/1, because if the race collapses into late errors and tired legs, a horse with that sort of raw performance signal can suddenly look very obvious—after the fact, of course, when it’s too late to sound clever.

And as the old line goes—sometimes attributed to the Bible and sometimes to racecourse bar-stools—“the race is not always to the swift,” especially not on Heavy ground at Newbury.

11) Weekend Warrior — outsider (20/1+)

Philip: Right, my Weekend Warrior pick—pure narrative, borderline irresponsible, and entirely for the post-race gloating rights: Dance And Glance at 20/1. Low weight, the sort of name that sounds like a midweek rom-com, and on Heavy ground I like the idea of a horse who can keep doing the same thing for longer than the others can tolerate it. He’s not in Mick’s muscle memory, not central to Pearl’s clean causal story, and he’s barely in the market’s inner circle—but if he’s the one still dancing while the rest are only glancing at the line, I’ll be unbearable until at least Tuesday.

12) Quick racecard crib

  • Race: William Hill Hurdle (Handicap Hurdle)
  • Course: Newbury
  • Time/Date: 15:20, 2026-02-07
  • Distance: 2m 69y
  • Going: Heavy
  • Field size: 16 runners (maximum)
  • Winner’s prize: £87,219

13) Guide odds (selected runners)

Runner Current odds
Let It Rain 5/2
Un Sens A La Vie 6/1
Tutti Quanti 15/2
All In You 7/1
Lanesborough 8/1
Hot Fuss 12/1
The Hardest Geezer 18/1
Bubble Dubi 22/1
Wreckless Eric 25/1
Dance And Glance 20/1

14) Web Sites (Alphabetical)

Gamble responsibly; Heavy-ground handicaps are high-variance by design.

Generated by Hippos Handicapping Preview Panel - Poe API v1.00.00 [ https://vendire-ludorum.blogspot.com/ ]

Posted by at
Labels: ,
Subscribe to: Comments (Atom)