Odds Compilers Are Being Replaced by Algorithms - What Does That Mean for Finding Edges?

[SharpEddie47]

The transition has been happening for years but I want to talk about what it actually means practically.

In 2005 the opening price on a Cowboys game was set by a human being. Someone who knew the NFL, had opinions, made judgment calls, and occasionally made systematic errors you could identify and exploit.

The human compiler who consistently underrated road underdogs in divisional games. The one who overweighted recent form relative to schedule strength. The one who hadn't fully processed an injury report by opening time on Tuesday.

These were real edges. Documented in my records. Exploitable repeatedly until they closed.

They closed because those humans were replaced by models.

The models don't have the same systematic errors. They have different ones.

The question is whether the algorithmic blind spots are identifiable and exploitable the same way human blind spots were.

My working hypothesis after five years of trying: the algorithmic errors exist but they're harder to find, less persistent when found, and correct faster when exploited.

What's everyone else seeing.

 

[FadeThePublic]

The public money pricing question is the one relevant to my approach.

Human compilers priced public sentiment inconsistently. Some were aggressive about shading lines toward public action. Some weren't.

You could develop a map of which operators adjusted aggressively for public money and which didn't.

The gaps between operators who shaded and operators who didn't created cross-book arbitrage opportunities around public-heavy games.

Algorithms have largely eliminated this inconsistency.

Major operators now use similar algorithmic approaches to pricing public sentiment. The gaps between them have compressed significantly.

The cross-book inconsistency that was exploitable in 2010-2015 is mostly gone.

What remains: the algorithm's model of public sentiment is built on historical data. When public behavior changes in ways the model hasn't seen before, the pricing can lag.

The new sport for fading the public is finding moments where public behavior is genuinely novel and the algorithm's historical training is stale.

Those moments exist. They're rarer and shorter-lived than they used to be.

 

[Klaus]

The Bundesliga algorithmic transition has a specific timeline I can track against my model's performance.

2015-2018: meaningful edge identifiable in opening prices. Human compiler systematic errors visible in specific market types.

2019-2021: rapid efficiency increase. The operator I primarily used for opening price value switched to predominantly algorithmic compilation in this period.

Post-2021: the opening price value largely gone. The edge in my approach shifted from exploiting opening price errors to identifying closing line divergence from my model.

The human compiler errors were systematic and learnable.

The algorithmic errors are different. They exist but they're statistical rather than systematic. Emerging from specific training data gaps rather than human cognitive patterns.

The algorithmic blind spots require different methodology to identify.

I'm still building that methodology.

 

[oli_sussex]

The exchange is an interesting case because it was never human-compiled.

Betfair's prices emerge from the interaction of participants. The "compilation" is the market itself.

In principle this should produce the most efficient prices available because it aggregates all participant information.

In practice the exchange has specific inefficiencies that differ from both human-compiled and algorithmic prices.

Late market information isn't always incorporated quickly because it depends on participants acting on it.

Thinly traded events have wide spreads because there aren't enough participants.

Algorithmic compilation covers events the exchange can't cover efficiently because participation is insufficient.

The exchange, human compilation, and algorithmic compilation each have different failure modes.

The sophisticated bettor maps which failure mode applies to which market and targets accordingly.

 

[TaffyTipster]

Not sophisticated enough to have tracked this directly.

But I've noticed something at the level of rugby prices.

The Premiership and Six Nations markets: prices feel tighter and move faster than they used to.

Matches involving smaller Welsh clubs in the Challenge Cup or lower-tier European competition: prices still feel soft. Still move in ways that suggest less computational attention.

The top markets got the algorithm. The bottom markets still feel like someone less attentive is compiling them.

Whether that someone is a human or a less sophisticated algorithm I don't know.

But the quality differential between top and bottom is visible even to someone who doesn't track it systematically.