Trying to predict the outcome is an integral part of sports. Everyone tries to guess the winner, be it for fun or gambling. Calling the winner is especially tough in combat sports, where everything can turn around in a split second – your guy can be dominating
the opponent for three straight rounds only to get knocked out the next second. Clearly, humans are ill-equipped for making complex predictions; some outside help is in order.
Their superhuman processing power gives computers a demonstrable analytic edge and makes them far more accurate predictors than puny humans. With the computing power of a million human brains, AIs can extrapolate seemingly chaotic and unfathomable things like future weather. In this article, we have concerned ourselves with the possibility of using AI machine learning to reliably predict the outcome of UFC fights.
Gathering pre-analytical data
Intelligence is in knowing what to calculate, not how to calculate. Computers may be far better than us at crunching numbers, but before true artificial intelligence becomes a reality, computers have no idea what to calculate. A regular old human needs to feed necessary data to a computer before it does its magic.
This starts with deciding what sort of data you need for your analyses in the first place. In our case, this would include the fighter’s profiles – age, stance, etc., and their statistics – fighting record, winning streak, etc.
Another activity involving pre-analytical data gathering and analyses is essay-writing. With writing, the hardest part is starting, and it’s not always easy to pick up momentum. Luckily, you can now get essay example free from a number of online quality writing services.
Qualitative analyses
Dry facts and stats are useful and all, but if they were enough, we wouldn’t need the fight at all; we would just calculate the result instead. Nuance to life lies in the unquantifiables, and for more precise predictions, you will need to account for qualitative factors that can not as simply be expressed in numbers. Humans have built-in thinking mechanisms for this, but they are very tough to quantitate and incorporate into a program. To give you a sense of what we are on about, here’s a sample list of what kind of questions you will need to consider :
- Is a particular fighter at his prime? or on a declining trajectory?
- What’s on the line? Is this a championship fight? And how does it affect either fighter’s motivation?
- Has a fighter gone through an emotional event recently?
- What do their body language and micro-gesturing indicate?
Furthermore, it’s not enough to evaluate the fighters individually; you also have to conduct comparative analyses. Fighter A might have won all previous fights, and fighter B could be a total statistical loser, but how will A stack up against B, specifically? Has A fought a similar fighter before? With the same height, weight, experience, stance, etc. combination. Maybe B’s fighting style is especially effective against A? All these can be decisive clues and insights.
For now, we do not have an AI advanced enough to incorporate all such considerations into its program, but if we trust technology trend predictions, these prospective advancements are well on their way.
Modeling & Projecting
One way to make your prediction more precise is to feed the AI with starting variables, let the simulated projection of a fight play out over and over with slight tweaks, and gather statistics on which one won and how many times. This will allow you to derive a percentage likelihood of a particular winner. Every time you predict wrong, you can try to analyze what factors you most likely overlooked, enhance the program, and go again.
Final thoughts
Trying to use computers to predict sports results is no novelty. Bookmakers have been doing it for years. Machine learning and AI are evolving at an exponential pace, and such predictions are becoming more informed and precise with every new iteration of a predictive program.
For those of you who primarily value sports for intrigue, don’t worry; we are still lightyears away from predicting the outcome of sports with anywhere near a hundred percent certainty. You can know the speed and trajectory of an asteroid and reliably extrapolate its crash site to the nearest meter, but things like long-term weather, future human behavior, and the outcome of sport are just too chaotic and multi-variable for complete predictive analyses. We are constantly going to get better at it, but never perfect. A supercomputer would need an insane amount of contextual data to foresee a squirrel running out on the field, for example, or a player having a sudden heart attack.
Diane Sherron is a sports journalist and a former athlete herself. She has played tennis for her entire life and was thinking of turning pro before being convinced by her parents to pursue a more practical career. Luckily, she had another passion reserved in store and grew to become a successful journalist.