Football fans are fickle — no doubt about it. One week we’re feeling on top of the world and praising every aspect of our club from top to bottom. The next, we’re questioning the players, tactics, management, the club hierarchy — even our own supporters.
I have a theory about the up/down nature of football fandom: sentiment is largely determined by the outcomes of the past three games. That’s to say that I believe we can model the public opinion of any given club by focusing entirely on recent results, ignoring historical performance.
Shifts in football perspectives can be so radical and jarring that I’ve questioned how this impacts the betting markets. Could there be opportunities which arise from football fans’ extreme recency bias? I’m going to put it to the test.
The Fickleness Of Football
A great example demonstrating the sharp changes in football fan perspectives took place at the Qatar World Cup 2022, which featured one of the greatest shocks in the tournament’s 92-year history.
Saudi Arabia defeated one of the tournament’s favourites, Argentina, 1-2 in the group stage in what was a major blow to Argentina’s dreams of lifting the trophy. To put it into perspective, Argentina had been on a 23-match unbeaten streak in competitive matches prior to the game, and bookmakers were offering 20/1 for Saudi Arabia to win.
So how did this shock result alter the public perspectives of Saudi Arabia and Argentina?
The odds on Saudi Arabia repeating that victory against their next opponents, Poland, were unsurprisingly nowhere near as big at 4/1. These odds represented a vastly improved perception of Saudi Arabia, who were ranked 51st in the World prior to the tournament, with Poland ranked 26th and coming off the back of a draw against Mexico. Saudi Arabia lost 2-0 to Poland, and then 2-1 to Mexico afterwards — failing to progress from the group phase.
Meanwhile Argentina were priced at 3/5 to bounce back from the shock loss by beating their next opponents Mexico. Yes, they were strong favourites — but perhaps still cautiously priced considering Mexico’s weak first performance against Poland, coupled with the fact that Argentina had not lost to them in ten competitive games. Argentina beat Mexico 2-0 and then won every game for the remainder of the tournament, eventually lifting the World Cup.
The point is, one game — one shock win for Saudi Arabia — entirely re-shaped how the public viewed the two teams. At at instant, Saudi Arabia became unlikely underdogs to cause an upset at the tournament while 3rd ranked Argentina’s impeccable form on the run up to the tournament was largely disregarded due to, what proved to be, one particularly bad day at the office. I recall the media stating that Lionel Messi doesn’t show up for Argentina, that he’ll never live up to Diego Maradona, and won’t lift the World Cup trophy in his career. Fast forward less than a month and the narrative couldn’t have been any different.
What Dictates How Fans Feel?
Without a doubt, results are the biggest factor impacting fan sentiment. Especially the most recent ones. Recent results are what build feelings of confidence, hope, uncertainty — or doom.
But there’s a few key elements within the recent results that contribute to what I call “fickle fan emotions”:
- Outcome. This is the straight W/D/L result. Naturally, Wins increase fan satisfaction levels, and Losses decrease them. The Draw tends to leave fans feeling neutral, or somewhat ambivalent.
- Goal margin. This is how many goals separated two teams. A high goal margin represents a comfortable win, while a low goal margin suggests a closer game. The goal margin is vital because it gives context to how superior/inferior a team was in a game. It impacts how strongly fans feel about a result.
- Opponent. Not all opponents are equal. A shock Win/Draw against a superior opponent creates more positivity for fans than getting a result against a low-ranked opponent. Likewise, a Loss or Draw against a lower-ranked opponent creates more negativity than losing to a high-ranked opponent. Fans appraise the value/meaning of results according to the opponents faced.
Importantly, determining how fans feel is not simple a case of interpreting the sentiment from social media, pundits, tabloids or any other publications. Instead a modelling process is needed in order to remove any bias. That’s precisely why I’ve incorporated all of the above factors into a mathematical model that I’ve named the Fickle Football Fan Formula (FFFF for short).
Fickle Football Fan Formula (FFFF) Explained
Fickle Football Fan Formula is essentially a fan sentiment scoring system. It’s here to demonstrate how we, as fickle football fans, tend to appraise upcoming fixtures on recent occurrences rather then the bigger picture. It identifies specific cases where there’s a huge disparity between the fan sentiment of two clubs facing one another, and recommends bets accordingly. For instance, when one fanbase feels completely hopeless, and the other highly confident. Or vice versa.
The scoring system is not based on where clubs view themselves historically, their manager, how many trophies they’ve won, or how many expensive players they’ve bought. It’s based on the recent outcomes — factoring in goal margins, and the quality of the opponents faced.
Ultimately the model aims to determine whether the fickleness of football fans and the football media impacts the accuracy of the match odds markets, and if this presents opportunities for bettors to capitalise.
I’m hypothesising that fan/media hype — both positive and negative — pushes the odds too aggressively in one direction, and that ‘momentum’ from recent games is not an overriding factor at play in the sport.
I’ve set the model up to suggest Lay bets against teams with a significantly higher fan sentiment score than their upcoming opponent (e.g. highly optimistic vs hopeless), and to disregard cases where similar sentiment levels exist. Essentially I’m betting against short-term fan positivity/negativity, looking to Lay lower-than-fair odds.
To maintain consistency and fairness, each of the suggested Lay bets is produced after each round of fixtures has played out.
The main weaknesses in FFFF is that the odds taken are not fully evaluated. So how can I be sure whether I’m taking value?
Truthfully, I can’t be. However, I’ve experimented with the Betfair Premier League Match Odds markets several times before, and have shown on this site that the prices are — on average — extremely fair. So fair, in fact, that I broke even with a 0.00% ROI after making hundreds of bets. Furthermore, I’ve found that generating my own prices using mathematical models has not succeeded in consistently identifying value from the match odds markets for top flight leagues; the odds were too sharp for me to find an advantage.
Needless to say I’m resigned that only highly specific bets — where the majority of the emotionally-charged public has got it drastically wrong — will be required to produce a profit from this market. That’s what FFFF aims to find through the application of it’s recency bias selection model. So its one main weakness could turn out to be its strength.
What I Hope to Achieve
FFFF is just an experiment. It will achieve one of the following:
- Profit: if the model is able to generate a profit over the long-haul, then it proves that recent fan sentiment has a significant impact on the betting markets. This is the best outcome.
- Loss: if the model generates a substantial loss, then it points towards developing an opposite model based on the momentum gained from recent results. This is yet another great outcome.
- Break even: if the model breaks even, then it suggests that there are many more factors at play. This is is worst outcome, because I’ll be exactly where I am now.
Regardless whether FFFF succeeds, I’m enjoying the process of running it and monitoring the drastic fluctuations in fan sentiment after each round of fixtures. As a Spurs fan, I can attest that it’s quite accurately mirrored my personal ups and downs this year so far!
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