Of all the different markets available for each grand prix to bet on, few match safety car betting for simplicity. Simplicity in not only understanding the bet itself, but also in its handicapping.
In fact, if there was any one single market that was compulsory for anyone wanting to bet on Formula 1 to master before moving onto other markets it would be betting on whether there will be a safety car appearance at each grand prix. But what makes safety car markets a great place to learn how to handicap markets?
Put simply it’s the fact there there are only two options available to bet, and the relative ease in which these options can be assessed. No only that, you’re assessment can go as deep as you’re willing to delve. Your model can be very simple and put together in a matter of minutes, or quite deep and complex requiring hours of research and number crunching.
But regardless of what model you come up with you will gain (perhaps for the first time in your punting career), an understanding of the very fundamentals of handicapping, probability and most importantly expected value. These, along with bankroll management are the cornerstones of any successful punter.
First of all we need to identify what we are hoping our model will develop. Of course this is very easy, we want to know the probability of a safety car being deployed in a given race. To help us we will use the example of the Australian Grand Prix being held in Melbourne (note the specific circuit, not just grand prix as some countries have multiple circuits), and try to develop a model which we think will accurately predict whether there will be a safety car during the race.
The first question you have probably asked is “was there a safety car in last year’s race?”
It so happens there was! But of course this isn’t quite enough information to give us the probability of there being one in 2019, it obviously to anyone who has watched any motorsport that the probability of a safety car isn’t 100%.
We obviously need more information. This is where you will need to do some of your own work as unfortunately there aren’t many readily available safety car analyses (at least that I have found). Again you will need to identify exactly what information you are looking for before beginning your search. Are you looking for the number of races with a safety car? The number of safety car appearances? The reason for each safety car appearance? Each research question will have its own unique answers and degree of difficulty to find.
For simple “was there a safety car in a grand prix” style statistics the best resource I have found is Fistats. If you want more in-depth knowledge of why there was a safety car, or how many appearances it made during a race you may have to collate these yourself from race reports. But for now I am happy to know in which races the safety car appeared at least once.
So back to our example, I now know that not only did the safety car make an appearance at the Australian Grand Prix in 2018, but also another four times between 2015 and 2017. This gives a total of 5 appearances from 7 races, or 71% of races.
Problem solved! The probability of a safety car in the 2019 Australian Grand Prix is 71%.
Or is it?
Seven races is not a very large sample size, what if in the five races with a safety car it was raining? Or maybe there were simply cars stopped in unfortunate positions purely by bad luck? Or what if there were kangaroos on the track in some years which have since been relocated?
The fact is from raw numbers we simply don’t know the answers to those questions. The 71% may be an accurate probability, but we can potentially make it better.
Across all 132 races the safety car appeared in 58 (44% of races). Comparing this to our earlier number we see that on average a safety car is 33% more likely to appear in Melbourne than the average grand prix. This large difference however shows us that simply the average probability of a safety car appearance in a grand prix isn’t enough to give us an accurate prediction.
But with just this little amount of information we can actually develop a reasonable (yet simple and possibly not profitable), model. By combining the probability of a safety car in Melbourne, with the probability of a safety car appearing at any grand prix we can reduce the chance of random events skewing our model, yet still account for the higher historical occurrences in Melbourne. By combining the probabilities we are left with a 58% chance of a safety car. This model may actually pass on its own (but would need to be tested, see below), but again I think we can do better.
To make our model even more accurate I am interested in why a safety car is 33% more likely in Melbourne than the average grand prix. By knowing this, I may be able to more accurately predict not only our example race, but races at all other circuits.
So what do I know about Melbourne as a circuit that would allow me to categorize it with other circuits as well as differentiate it from others? It is a street circuit.
Street circuits are notorious for their high number of safety car appearances. Their close walls and surfaces without built up rubber are difficult for drivers and leave very little margin for error. In theory more accidents means more safety cars, but is this the case in reality?
To test this we compare the appearances of safety cars at street circuits in our sample (Melbourne, Monaco, Montreal, Valencia and Marina Bay), to all other circuits. While street circuits made up only 32 of our 132 race sample, they accounted for 23 of the 58 safety cars. We would expect a safety car to appear in 72% of races held at street circuits compared to only 35% at other tracks, a massive 37% difference!
It seems likely that my hypothesis about increased difficulty for the driver may have something to it. With this in mind there is another variable we can analyse which can play havoc with even the best of drivers. Rain.
Again we run an analysis of our data to see in which races it rained so we can compare the probability for wet and dry tracks. Of our 132 race sample it rained in 21 races (although our statistics do not give us the severity of the rain), in which 62% of races saw a safety car. For the remaining 114 dry races the safety car was only required on 45 occasions (39%). So again, it seems as though the tougher a race is for drivers, the more likely a safety car is to appear.
Breaking down our analysis by weather and circuit type again shows that wet races have a higher percentage of safety cars (75% for street circuits, 59% for permanent circuits), compared to dry races (65% for street circuits, 30% for permanent circuits).
Digging this deep and purely relying on percentages though begins to get dangerous. While it may seem like a good idea to model a wet street race at 75% probability of a safety car, this is only drawn from a sample of 4 races. To ensure that our comparisons are statistically significant it is possible to run a Chi-square test to see if our results are significantly above the normal expected frequencies (ie our number of safety cars would occur less than 5% of the time purely by chance).
Unsurprisingly the weather comparisons on street circuits was not significant with such a small sample size. However wet tracks verses dry tracks was significant for permanent circuits, and only sightly outside significance (p=0.056 for the stats heads), across all circuits. The difference in safety car appearances on street circuits compared to permanent circuits was also significant.
We are now armed with some pretty important information to help us develop our model. Street circuits are 37% more likely to have a safety car than a permanent circuit, and a wet race on a permanent circuit is 29% more likely to have a safety car than a dry race. We also know that weather makes no difference in whether or not there is a safety car on street circuits.
Previously on our very simplistic model of combining the average safety car appearance in Melbourne with the overall average probability of a safety car gave us a probability of 58%.
Now knowing that street circuits have a much higher probability (72%) of a safety car occurrence we can combine that with the average Melbourne safety car appearance (71%), to give us a new probability of 71.5%. To convert this into a betting odd we simply divide 100 by our probability to get $1.40. So when looking to place a bet on there being a safety car in Melbourne we would want to do so on odds greater than $1.40 to ensure we are placing positive expected value bets and will win over the long term (assuming our model is more accurate than the bookmakers). Conversely if we wanted to bet on there being no safety car in the race the probability would be 28.5%, or $3.51.
The above model certainly may not be a path to riches, or it may be! As I stated earlier in the article, a much more simplistic model may actually be enough to beat the bookmakers. To know this it is important to test many model you have and you can do this a number of different ways. One way is seeing if you were able to correctly predict whether there was a safety car in a race and whether these predictions were better than chance.
Another is to compare your modeled prices to bookmaker prices and see if you were able to profit over a period of time (remembering only to bet when there is value). This requires access to previous betting market data which could be very difficult to source for a market such as this, however you can also “paper bet” you model moving forward to see how it tracks.
In back testing our model above on the 2019 Formula 1 season the outcome with the highest probability was correct in 15 of the 19 races. This is above the expected strike rate of 50%, however does not guarantee that the model would have made a profit.
It’s also important to remember that when doing any sort of back testing do not include data from the events you are testing as this will corrupt your results and could be a very costly mistake!
By no means is the model described above perfect, it is merely something to get you thinking about what you might be able to develop yourself. With more time and the willingness to dive into statistics it would be possible to investigate any number of factors which could also be incorporated into your model.
Do rookie drivers crash more early in the season? Do cars have more failures earlier in the season or later in the season as components age? Are there more crashes when certain drivers are buried back in the pack after qualifying?
Any number of possible factors could be considered and incorporated into your model. And the great thing is once you learn to predict whether there will be a safety car in a Formula 1 race, it becomes much easier to predict many other markets.
Those wanting to try their luck with a punt on the safety car appearing at Grand Prix throughout the year can find markets available at 1xBet, Centrebet and William Hill.
