How Mathematics Took Over Sports
Mehul Kapadia, managing director of F1 Business, Tata Communications looks at game changing data and how numbers influence sporting decisions and outcomes.
On the face of it mathematics and sports appear poles apart. Traditional stereotyping would tell you that the maths whizz-kid doesn’t hang out with the football players in the school playground. Sport is considered an art form rather than a science. Decision making is emotional or experiential rather than logical or evidentiary. Development and improvement relies on practice, technique and teamwork. In team sports, decisions on recruiting new players are based on an educated eye for talent rather than number crunching, research and analysis.
If you look more closely, however, the gulf between maths and sports is not the chasm it appears at first sight. Mathematics and mathematicians are increasing the speed and quality of human decision making across a number of sports, but the most obvious example is Formula One racing. On race day, it’s all about numbers. Engineers will perform hundreds of calculations to formulate the best race strategy based on variables such as tyre pressure and tread; fuel load; race and lap distance; car and driver weight. Before taking to the track, the driver, car and fuel load must be a minimum of 702 kilograms – with cars losing on average 2.5 kilograms in fuel each lap. How the fueling and weight strategy is managed can be the difference between winning and losing.
The fastest pit stop of all time was recorded at 1.923 seconds by Red Bull Racing in 2013. However, with the 60km/h speed limit of the pit lane, realistically the average pit stop costs 15-20 seconds. The in-race changes you make to the car must claw that time back – and preferably go even further – so it is vital that teams pit their drivers at the right time and make the right changes. In order to do this, the only way is maths.
Making sense of the numbers
The mathematics of an F1 race is both fluid and dynamic. In layman’s terms, the numbers are changing all the time. F1 telemetry systems are constantly collecting data on all manner of racing variables: oil and water levels, clutch fluid pressure, G-force and engine revs per minute to name a few. These are communicated back to engineers in the pits and at control centres using a range of radio and wireless technologies.
Arguably, F1 is now the most data hungry sport in the world and maths influences the outcomes of races and championships more heavily than it does in other sports. Therefore, the stakes are high for the developers of new technologies which make the process of using data and calculating the necessary sums more sophisticated and efficient. In order to help stimulate such innovation, initiatives such as the F1 Connectivity Innovation Prize challenge teams of innovators to propose solutions to current technological challenges posed by the sport.
One stat that matters
Ultimately, the myriad of numbers and calculations all feed into a select few benchmarks that define F1 success. Where in the race did you finish? How many championship points did you earn? A common perception from pros and ex-pros who have been there and done that is that the numbers can lie. Who would employ a mathematician over a former championship winning coach? Statistics are interesting snippets of information that sound great when a commentator rattles them off at a moment’s notice but it’s people who win at sport – right?
One example to the contrary comes from baseball. The Oakland A’s calculated their way from a regional division challenger to a play-off powerhouse between the years of 1996 and 2004 – a period now referred to as ‘the Moneyball years’. Given Oakland’s limited budget for salaries compared to other franchises, they needed to source players that were either undervalued or were showing potential but had gone unnoticed by rivals with deeper pockets. The statistics commonly used at the time such as batting averages, runs batted in and stolen bases did not provide an adequate basis for doing so. In light of this, the A’s set about developing a rigorous process of statistical analysis, delving deeper into the stats that gave them more solid foundations on which to assess player performance.
On-base percentage, a measure of how often a batter reaches base for any reason other than a fielding error, unveiled a meaningful way of measuring a batter’s strike rate. Slugging percentage, a mathematical equation that calculates the total bases divided by at bats (when the batter is batting against a pitcher), more accurately determines the power of a hitter than a statistic such as number of home runs. Using such figures to recruit potential stars at a fraction of their true market value, the Oakland A’s emerged as the powerhouse of the American League – not bad for a franchise operating with around a third of the financial clout of the New York Yankees.
Calculating success
Numbers are everywhere in sport and in a way they always have been. The most important piece of information about any sports event is usually a number. Who earned the most points? What was the score? How fast was their time? How many times have they won?
Where the gap between mathematics and sports has existed is in the measures sports men, women and teams take in order to change the all-important numbers that define success or failure. But increasingly we are seeing the role of data and analysis being used to find winning tactical formulas and find the best-value talent.
