Justin Fisher
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Justin Fisher
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Justin Fisher é Cientista de Dados, pós-graduado em Análise de Dados e com MBA em Inteligência Artificial e Big Data. Atua na área desde 2015, com experiência em empresas como Itaú Unibanco e RD Station. Também é professor e mentor em Análise e Ciência de Dados.
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How Often Does the Best Team Win? | Check Out the Statistics!

Cover image for post Find Out The Real Chances Of Your Favorite Team Winning Football Matches
Find Out The Real Chances Of Your Favorite Team Winning Football Matches
How Often Does the Best Team Win? | See the Statistics!

In football, the evaluation of teams' performance extends beyond the outcome - the wins and losses; it is a realm also governed by intricacies and statistics. One inquiry we can pose is:

When a team is labeled as a "favorite," what are their actual chances of winning?

This notion of a "favorite" is not solely based on popularity or history, but rather on the teams that, according to statistical indicators, possess an advantage in a specific showdown. It goes beyond mere reputation or past performance and delves into the realm of objective data analysis. These indicators provide valuable insights into the potential outcome of the matchup, allowing informed predictions to be made. By considering a range of factors such as team form, player statistics, and recent head-to-head results, a more accurate assessment of the favored team can be established. This analytical approach aims to minimize subjective biases and offer a more objective perspective on the expected outcome.

In order to deepen our understanding and address this inquiry, we delved into an in-depth analysis encompassing over 22,000 matches throughout recent years. By immersing ourselves in this comprehensive study, we sought to unravel the intricacies and shed light on the underlying factors that contribute to the phenomenon at hand. Through meticulous examination and scrutiny, we aimed to uncover patterns, trends, and correlations that might provide valuable insights into the subject matter. This extensive exploration allowed us to gain a comprehensive perspective, enabling us to draw meaningful conclusions and potentially unlock new perspectives in the realm of sports analysis.

We have examined a vast collection of 22,058 matches played between January 2018 and September 2023. Immerse yourself in this extensive pool of data to gain valuable insights. Explore the nuances of each game, uncover patterns, and extract meaningful statistics. With this comprehensive study, you'll be equipped with the knowledge to make informed decisions and strategize effectively.

These matches hail from 14 diverse top-tier leagues around the globe, encompassing Spain, England, Italy, Canada, China, USA, and more.

In total, this analysis includes 388 different clubs.

In our database, we gather data such as the ultimate outcome of the match and the pre-odds from various BETTING SITES, crucial for comprehending the inherent probability of a specific outcome.

So, what are these odds after all?

In the realm of gambling, odds serve as a reflection of the likelihood of an outcome occurring. Essentially, they represent the prices set by bookmakers for each possible result of an event.

The potential return for each unit wagered is indicated by the odd value. For instance, a 10 odd implies that for every $1 bet, the return will be $10 (including the initial wager) if the prediction is correct.

Let's take an Example of a match between Manchester City and Burnley. Consider the odds being offered as follows:

  • Manchester City vencer: 1,35
  • Burnley win: 9.02
  • Draw: 5.35

At first glance, these figures indicate the potential earnings for each wagered unit if your prediction proves accurate. Placing a $1 bet on Burnley would yield a return of $9.02 in the event of their victory.

But what if we wanted to grasp the implicit probability within those odds?

The arithmetic is straightforward. Take the reciprocal of the odds to obtain the probability. For Burnley, it is 1 divided by 9.02, resulting in 0.1109 or 11.09%. This indicates that, according to the bookmaker, Burnley has an 11.09% chance of winning.

By repeating the procedure for Manchester City and the draw, we obtain probabilities of 74.07% and 18.69%, respectively. The likelihood of Manchester City winning stands at 74.07%, whereas the probability of a draw is at 18.69%.

However, when we add up these percentages, we end up with something greater than 100% - specifically 103.85%. This happens due to the bookmaker's margin.

Justin Fisher
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Justin Fisher
Sobre O Autor
Justin Fisher é Cientista de Dados, pós-graduado em Análise de Dados e com MBA em Inteligência Artificial e Big Data. Atua na área desde 2015, com experiência em empresas como Itaú Unibanco e RD Station. Também é professor e mentor em Análise e Ciência de Dados.

Quantifying probabilities and outcomes reveals that the notion of a "favorite" is not merely subjective but rather measurable. When examining the likelihoods and results, it becomes evident that the concept of a frontrunner can be objectively evaluated.

To gain a more unadulterated insight into expectations, we must reallocate these probabilities so that they add up to 100%. This can be achieved by adjusting each probability proportionally to its share in the overall sum.

For instance, the likelihood of Burnley winning would be recalibrated as follows: (11.09 / 103.85) × 100 = 10.67%. Consequently, this adjusted probability implies that Burnley has a 10.67% chance of securing victory.

By employing the identical approach to both Manchester City and the draw, we procure adapted likelihoods of 71.33% and 18.00% correspondingly. The application of this methodology ensures that the chances of success for Manchester City are significantly higher compared to the possibility of a draw.

When analyzing the probability distribution among teams and how it manifests in match results, it becomes evident that the notion of a "favorite" is not purely subjective but rather quantifiable. It is apparent that the distribution of probabilities has a direct impact on the outcome of games, highlighting the measurable nature of the concept of a favorite.

The difference in odds and the likelihood of victory for each team unveils crucial details regarding the expectations prior to a confrontation. To enhance the accuracy and strength of this analysis, we have established a criterion centered around the variance in probabilities.

Let's classify as the "favorite" team that has an advantage of at least 5 percentage points over their opponent. To determine this, we analyze various factors such as team strength, previous performances, and current form. The team that demonstrates a consistent and superior performance is considered the favorite. It is crucial to take into account the team's overall statistics, individual player skills, and tactical strategies employed by the coach. By evaluating these aspects, we can identify the team that holds a significant advantage and is more likely to succeed in the match.

For instance, in a scenario where Team A has a 37% probability of winning and Team B 33%, the margin is merely 4 percentage points. Hence, in this particular match, we do not ascertain a distinct frontrunner.

In the event that team A has a 40% probability compared to team B's 30%, the difference jumps to 10 percentage points, thus identifying team A as the favored contender of the match.

How often do the favorites win?

In 54.2% of the matches where a favorite was determined, the favorite emerged triumphant. The victor prevailed in the majority of instances when the odds favored them. It can be observed that more often than not, the expected winner emerged victorious. Out of the matches where a clear frontrunner was identified, the favored team clinched the victory in a significant percentage of cases, amounting to 54.2%. The outcome of the matches largely aligned with the predictions, as the favored team secured the win in the majority of instances.

Out of the analyzed 22,058 matches, we have identified favorites in 19,596 of them, accounting for 89% of the total. The most insightful finding from this research indicates that, among these matches where a favorite was determined, the favorite emerged victorious 54.2% of the time.

This figure alone signifies that the odds, as they mirror probabilities, offer a rather precise but not foolproof glimpse into what to expect on the field.

Clearly, football, with its wealth of intricacies and unpredictabilities, remains a sport where surprises occur and logic is occasionally challenged. It is undeniable that the beautiful game is replete with an abundance of nuances and unexpected turns, constantly defying conventional wisdom. The very essence of football lies in its ability to amaze, confound, and captivate, leaving spectators in awe of its enchanting allure. The pitch becomes a theater of unforeseen narratives, where underdogs rise to prominence and champions falter. In this ever-evolving landscape of passion and skill, the only constant is the thrilling uncertainty that awaits each match, inviting us to embrace the mystique and thrill of the game.

But let's delve a bit further: are there any disparities depending on the country? And the answer is indeed affirmative! However, when considering various nations, variations do come into play.

When we examine the graph below, we can discern a fascinating spectrum of victory rates, ranging from approximately 48% to nearly 60%.

Most Predictable Leagues

China and the Netherlands top the table, suggesting that in their respective leagues, the favorites often meet expectations and win more frequently. This may point to a discrepancy in strength among the clubs in these leagues, with some teams asserting dominance.

Predictability Average

Leagues such as Spain, England, Italy, and Canada demonstrate a greater equilibrium, where the favorites still emerge victorious on a regular basis, but there is also room for surprises. These highly followed leagues worldwide exemplify that even with prominent clubs and substantial investments, the competition remains fierce.

Most Unpredictable Leagues

Remarkably, Argentina stands out at the lower end of the table. In leagues such as this, the outcomes can be less predictable, suggesting a more evenly matched competition between clubs or the influence of other external factors on the game.

And what about over time? Taking an average per month of the winning percentage of the favorite team throughout the nearly six years of data, we can observe fluctuations in the performance of the teams deemed favorites.

While at times they surpass expectations, at others they fall short, showing no clear tendency and remaining close to a 50% average. Sometimes they exceed what is anticipated, while other times they disappoint, lacking any discernible pattern and maintaining a nearly 50% mean. On occasion, they outperform predictions, whereas in other instances, they underachieve, displaying no specific inclination and hovering around a 50% threshold.

Finally, taking into account the dynamics and unpredictability of football matches, we can assess to what extent pre-match probabilities align with actual results. Do you remember the percentage point difference we calculated earlier?

In this perspective, each bar groups the matches within a specific range (for instance, 0-10% encompasses the matches where the favorite had a difference of 0 to 10 percentage points).

The variation in probability among teams offers a clue as to how much one team is favored over the other. This indication of favoritism can be deduced from the disparity in probability between the teams. It serves as a measure of the extent to which one team holds an advantage over the other. The difference in likelihood between the teams serves as a gauge of the level of favoritism exhibited by one team over the other. It provides insight into the degree of superiority one team has over its opponent.

But does a clear favoritism (or the lack of it) effectively translate into victories? Let's examine the data to comprehend this correlation.

The analysis results provide a clear understanding of the correlation between the disparity in odds for the favored team to win and the actual outcomes of the matches. This comprehensive examination offers valuable insights into the connection between the probability differentials and the ultimate results achieved on the field. Through meticulous evaluation, a lucid understanding of the interplay between the likelihood of the favored team emerging victorious and the eventual outcomes of the games can be attained. The acquired findings shed light on the intricate dynamics at play, highlighting the significance of odds differentials in accurately predicting the actual results.

  • 0-10% Difference: In this range, the probability difference between the two teams is minimal, implying that both have nearly equal chances of winning. The result reveals that the favorite loses 51% of the time, ties in 30%, and wins in only 19% of the games. This suggests that when the probability difference is low, it is more likely for the favored team to lose than to win.
  • 10-20% Difference: Here, the favorite has a slight advantage in terms of probability. The outcome confirms that the favorite wins more often (44%) than loses (26%). However, there is still a considerable chance of a draw (30%).
  • 20-90% Difference: As we move towards higher probability difference ranges, we observe a clear trend: the percentage of victories for the favored team consistently increases, while the percentage of defeats and draws decreases. For instance, in the 80-90% range, the favorite team wins an impressive 92% of the time, highlighting that when there is a significant probability difference, the chances of the favored team winning are extremely high.


Therefore, as the disparity in probabilities between the teams widens, it becomes increasingly likely for the favored team to emerge victorious.

Intuitively, it is logical that a larger probability difference signifies a stronger belief that one team is superior to the other. This belief is based on the understanding that when the gap in probability widens, the confidence in the team's superiority grows.

However, when the margin is narrow, the outcomes become more uncertain, and the favored team is more likely to face defeat. In such cases, even the slightest discrepancy can significantly impact the final result, making it a thrilling and unpredictable game. Therefore, it is crucial for teams to remain vigilant and focused, as even a minor lapse in performance can turn the tide in favor of the underdog.

In summary, this analysis unveils that the notion of "favorite" extends beyond a mere subjective label; it is a quantifiable measure founded upon probabilities. To put it briefly, this examination demonstrates that the concept of a "favorite" surpasses being a mere subjective designation; instead, it is an assessable metric that relies on probabilities. This assessment, in essence, reveals that the term "favorite" transcends a mere subjective categorization, but rather embodies a quantifiable measure hinged upon probabilities. In concise terms, this scrutiny uncovers that the idea of a "favorite" surpasses a mere subjective classification; instead, it is a measurable criterion rooted in probabilities. In a nutshell, this evaluation exposes the notion of a "favorite" as more than just a subjective label; it is, in fact, a quantifiable measurement that rests upon probabilities.

We have uncovered that, as per the established parameters, the favorites emerged victorious in over half of the analyzed matches, suggesting that the odds provided by the betting agencies accurately reflect the expectations of victory.

Nevertheless, football's unpredictability endures, with surprises occurring in a sizeable portion of matches. However, the element of surprise remains inherent in football, as unexpected turns of events often unfold during a significant number of games. Despite attempts to decipher its intricacies, the sport consistently defies expectations, leaving fans and analysts alike captivated by its thrilling nature.

Learn more about the author: Canada's Legal Betting has a new Statistics Expert.


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