How to Use Betting Models

In the age of analytics, some bettors turn to statistical models or even machine learning to predict fight outcomes. Using a betting model means you input data about fighters and fights, and the model outputs probabilities or picks – ideally more accurately than the betting market. Here’s a basic roadmap on using models for boxing:

* Gather Data: A model is only as good as the data feeding it. For boxing, data can include fighters’ records (wins, losses, KO%, etc.), physical stats (height, reach, age), and fight stats (punch output, accuracy, defense percentages, knockdowns, etc.). You may compile a dataset of past fights – who fought whom, outcomes, and stats from those fights. One ambitious project collected data on over 3.5 million rounds/fights and 370k+ boxers! You don’t need that much to start, but more data helps. You’ll want to capture factors you believe influence fight results. For instance, reach advantage, southpaw/orthodox matchups, age difference, recent activity, power punch landed stats, and so on.

* Choose a Model Type: Simple models might be logistic regression (predict probability of Fighter A winning) based on variables like reach difference, age, KO ratio, etc.. More complex ones could be machine learning algorithms (decision trees, random forests, neural networks) that find patterns in the data. Some have used Elo rating systems for fighters (similar to chess or other sports) – each fight adjusts their rating depending on expected vs actual outcome. Elo can give a baseline probability: higher Elo difference = higher expected win probability. Others have attempted Monte Carlo simulations by simulating round-by-round outcomes. If coding isn’t your forte, even creating a simple spreadsheet model weighting factors can be a start. The key is you’re formalizing your thinking: e.g., assign points for height advantage, recent KO losses, etc., and tally it up. It might not be high-tech, but it’s a model of sorts.

* Train and Test the Model: If you have historical data, you can test your model on past fights to see how well it predicts. For example, build your model on data from 2010-2019, then see how it would have done on fights in 2020 (that it hasn’t seen). Does it correctly pick winners at a higher rate than the odds-implied probability? Does it find underdog picks that the market missed? Refine your model if needed – maybe you find that including a fighter’s chin rating or defense (opponent connect %) improves accuracy. Some advanced modeling have found notable edges: one deep-learning model reportedly achieved a 22.5% ROI on boxing bets during back-testing, which is huge if true. This shows the potential if you strike gold with a model.

* Use the Model’s Output for Betting: Once you trust your model to some degree, incorporate it into your betting decisions. The model might spit out, say, “Fighter A 65% win probability, Fighter B 35%” for an upcoming fight. If the odds on Fighter B imply only 20% (i.e. +400) and your model says 35%, that’s a value bet on B. Model-derived probabilities essentially become your “true odds” baseline. Compare them to bookmaker odds to spot discrepancies. The model can also help on over/unders or method of victory if you build it for that – for example predicting chance of KO based on combined KO rates, etc.

* Caution and Context: A model is an aid, not an oracle. Always apply context that numbers might miss: Has Fighter A changed trainers? Did Fighter B have an injury or a bad weight cut? Models work off the data given; they might not know a fighter was sick last fight or moving up in weight for the first time. Use your boxing knowledge in tandem. The model might highlight an overlooked underdog, but you should still do qualitative analysis (watch footage, etc.) to confirm nothing big is off. Also, be mindful of sample size – boxing is tricky because elite fighters don’t have hundreds of matches like team sports have games. So, the data can be limited.

Building a boxing betting model can be rewarding. Even a simple one can give you a more objective view, cutting through bias. You might discover interesting predictors: maybe reach advantage correlates with winning only up to a point, or certain punch stats matter more. Plus, if you succeed, you basically have your own “algorithmic handicapper.” Some bettors have gone this route and seen great success, at least by their claims (one Redditor boasted 80% prediction accuracy with a machine model – take with a grain of salt, but it’s possible). Start basic: perhaps an Excel sheet scoring each fighter in categories. Then iterate. If coding is your thing, dive into Python/R and experiment with logistic regression or more. It’s a fun challenge that can sharpen your betting edge. And at the end of the day, even if your model isn’t perfect, the process of quantifying fights will likely improve your betting by making you think about factors and probabilities more systematically. In boxing, where “styles make fights,” turning those styles and stats into numbers can reveal angles you might otherwise miss.

Sources: Medium (mattobrien)