Tennis Betting Strategy: How To Use Statistics
Understanding the benefit of using data when assessing value bets in tennis is naturally, very useful, for successful betting.
While tennis punters tend to either be in one of two camps – ones who trust their eyes, and the others who use stats and data, even the former genre will almost always consider data in some way, shape or form.
Having established a website which supplies extensive tennis data, I thought I’d run through some of the various considerations when developing a tennis betting strategy and attempting to assess whether either player represents a value betting proposition.
Also included: Free Stats & Tennis Resources
When a match is listed on a website, or discussed on TV, it will be discussed as a generic surface, such as hard court, indoor hard court, clay court or grass court. These generic descriptions are often misleading, as the surface manufacturer, ball supplier and climatic conditions have a huge impact on the likely court speed.
One clay court can play considerably differently to another, for example. During the clay Masters season in the European spring, the event at Madrid (which is at some altitude) plays much quicker than the tournament in Monte Carlo.
With this in mind, non-clay courters who have a big serve are more likely to have success in Madrid than in Monte Carlo, which has conditions which suit return-orientated grinders.
Rafa Nadal loves slow conditions, and despite Madrid being in his home country, the quicker conditions haven’t been to his taste, recording a much worse record there than in those slower conditions in Monte Carlo.
Mathematically speaking, I am more concerned with the impact of the conditions than understanding the underlying reasons behind them. Looking at the aces per game mean, compared to the tour surface average, is useful, as is comparing service points won and service hold percentages with those tour mean numbers. Quicker condition venues will have higher figures for all three metrics.
Projected Hold Percentage
Neatly tying in with court speed is the projected hold percentage for a match-up. The reason why it neatly ties in, is due to the effect that court speed will have on these projected hold numbers.
Having understood the impact of court speed, modellers can use service/return points won percentages, or serve hold/break opponent figures to predict a projected hold percentage for both players, which can then be assessed to establish a model price for either player.
If these have a sizeable discrepancy from the available bookmaker prices, a bet can be considered, assuming there are no other external issues needing to be factored in.
Head To Head Data
In the media, a narrow h2h lead, such as 1-0, 2-0 or 2-1 is often mentioned as a positive for a given player. However, while it certainly cannot be a negative, tennis is a sport determined by very fine margins and having won one or two previous meetings isn’t a major issue at all – in fact, those media outlets pushing this as a valuable factor in pre-match assessment are often guilty of lazy journalism.
Previously, I discussed this with a former top ten player.
He had a disastrous head to head record against a particular opponent, yet he said that he felt that…
His h2h record had no bearing on his mental state prior to the matches.
He lost six consecutive deciding sets against that particular player (including numerous final set tiebreaks) and was in a dominant position in a number of these matches too. He said he felt very capable of beating that player and small margins determined his losses.
Understanding the context of head to head matches is key:
- Has the losing player lost a lot of tight matches in the series?
- Were they ranked much worse in those previous matches, before exhibiting current improvement?
- Did the leading player play matches on a surface which considerably favoured him?
All these factors need to be considered when using h2h data as part of any betting strategy, in addition to the player data from these previous match-ups.
Injury & Fatigue Considerations
Many tennis match-ups have fitness concerns for one or both players.
These might be short-term injury (perhaps the player retired in a previous match) or long-term injury – we’ve seen that Andy Murray and Stan Wawrinka, to give two recent examples, have had considerable difficulties even getting close to previous levels on their returns to tour.
Another concern here would be an arduous schedule, which could be from the current tournament (consecutive long matches, for example), or from previous weeks (perhaps consecutive runs to the latter stages in back-to-back events).
Travel considerations are also necessary to think about, particularly after Davis or Fed Cup weekends – frequently a player will have to fly halfway around the world and then have little time to acclimatise prior to their first-round match.
By building up a historical database of these situations it is possible to make a quantifiable judgement as to the effect of these various fitness concerns, which can then be used to make adjustments to basic model pricing.
Key Point Variance
The final point I would like to discuss is key point variance. In short, it is very rare for a player to be ‘clutch’ long-term, with the ability to convert and save break points usually rather variance-heavy.
For example, there are regularly players who have won 60% of their matches over a given time frame despite having won a 100% or fewer combined serve and return points won percentage. In the long run, this 100% figure should yield a 50% win rate, so that particular player is winning matches at a rate greater than their expectations, and in these spots, the player usually mean-reverts by losing some matches in the short or medium term.
Conversely, some players might look on the surface to be in a bad run of ‘form’, but statistics may indicate they are much better than these bare results.
Sam Querrey, on hard courts, is currently one. He’s winning more than 50% of competed points over the last 12 months but has lost more matches than he’s won – basically, he’s lost more than his fair share of key points. I’d suggest he should be able to turn that around in the not too distant future.
If you’re not taking these key data points into consideration when developing your betting strategies for Tennis then it’s likely you’re overlooking some of the most important data available to you.
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