![]() ![]() Logit regression analysis isolates the additional impact of form and easy fixtures on the probability of an outcome (in this case 10 points scored) occurring. Unsupervised machine learning helps identify who the maverick players are by sorting all players into different groups, based on the volatility of their points scores. We see these methods as complementary: Monte Carlo analysis explores the impact of a greater volatility of points on a manager’s prospects of catch-up by simulating what would happen in thousands of possible ends-of-season. Here, in our Data Deep Dive, we’ve highlighted some of the statistical and econometric methods they used. Our fantasy football tips are based on comprehensive analysis by our data scientists. Pogba’s distribution has a flatter peak, but ‘fatter tails’ – he’s more likely than Silva to collect a low score, but also more likely to score a super-high total. Silva is more likely to score a decent total – 5, 6 or 7 points – but is unlikely to bag a super-high score. The chart below compares the two players by showing their points distributions: the probability that they’ll score a certain amount of points. He’s inconsistent, but wouldn’t Pogba be more likely than Silva to score those crucial 12 points? He only bagged 2 points at home to Crystal Palace, but then scored twice away at Burnley and notched up a heavenly 13 points. Your attention turns to Paul Pogba, the maverick. But time is running out, and gradual accumulation won’t help you now – will Silva score the 12 points you need this week? Sure, this gradual accumulation has helped you stay within touching distance of the top. Silva has been a solid pick, with consistent returns of 4 to 8 points most weeks. Who might bring in the points you need? You spot David Silva, the safe bet. You scan your squad for players that the leader doesn’t have. To have any chance of catching the leader before the season ends, you need one of your differential players to score at least 12 points in the next Gameweek. Picture the scene: it’s crunch time in your mini-league. 'Fatter tails': How mavericks can help you catch up From a statistical point of view, gambling on mercurial players that have the capability of bagging super-high points totals is the most attractive strategy for the chasing pack at this stage of the season. So, with only a few weeks left, it’s time to roll the dice: if you want to move up the table, our experts suggest you pick maverick players with a high volatility of points. But with a battery of statistics on player performance available online, it’s unlikely there’ll be a pool of untapped, high-scoring players that have not already been snapped up by other managers. Those with a high average points tally might seem an easy starting point. But what type of players should you pick? ‘Differential’ players, as they’re known in the fantasy football community, are key to success. Let’s start by stating the obvious: to overtake the manager at the top of your league, you’ll need to pick different players to them. The busiest managers can jump straight to the tips, but to any budding data scientist, please do feel free to interrogate our data analysis in the Data Deep Dive further below – and let us know what you think! The best strategy? Gamble on mavericks So what’s the best way to pick up points fast? Frontier’s data scientists have crunched the numbers to provide the answers: which strategy to follow to catch your league leader, and which players to pick to do it. With only a handful of weeks left in the season, there’s not much time to climb the league table. And with the imminent restart of the Premier League comes a return to action not just for players and fans, but for 7 million fantasy football managers too. ![]()
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