Games can produce outcomes that seem to defy expectations for sporting organizations and teams, even when overwhelming statistics suggest a victory. A struggling underdog can often defeat a perennial powerhouse in a single game. Even the greatest players occasionally slip, losing to opponents in unpredictable ways in the closing stages of a game and forcing a reversal of fortune when all indicators suggest victory.
Unforeseen results in games and matches offer unique insights into decision-making and the implications of information availability. Sports analytics provides one particular perspective on human decision-making. Faced with uncertainty, incomplete information, and probabilities, individuals try to assess and make decisions about upcoming matches and games.
Why Good Decisions Don’t Always Produce the Expected Results
We study previous instances of your team and/or opponents to understand the patterns that should lead to success in future matches. As mentioned above, statistics from previous games describe what has happened in the past. They do not guarantee similar results in the future.
A basketball team that has recently won 8 of 10 games will have given teams that face them recently all the information they need to gain an edge. Much of their recent success likely came against weaker teams, and sometimes a team can have problems with a certain type of opponent that they will face in their next game.
Looking at a game from several perspectives can reveal details that a single analysis might overlook. General performance statistics provide useful context, while matchup-specific data helps explain how teams might perform against particular opponents. Readers can also compare these findings with free sports betting picks from Doc’s Sports to see how experienced analysts interpret the same information. Different assessments may lead to opposing predictions, even when both rely on sound reasoning. This highlights why sports analysis is more useful for understanding possible outcomes than treating any prediction as certain.
This fundamental rule of well-informed decision-making must be judged by the quality of the reasoning used to arrive at the forecast, not by whether the forecast turns out to be correct.
Understanding Probability Instead of Expecting Certainty
Moreover, predicting a certain outcome does not mean it will happen. In sports analytics, we can better understand the probability of certain events and make better-informed decisions based on the quality of the available data and the corresponding analysis.
This is how chances work. Even if a team has a 70% winning estimate (or better) and is expected to win, that still leaves plenty of room for the opposing team to score and win. This is why a loss does not necessarily invalidate a team’s prior win-probability estimates.
While people find it easier to digest information when there is a definitive answer, anticipating multiple scenarios with varying degrees of likelihood is far more accurate.
The analyst understands that even very strong analysis can be overcome by seemingly minor circumstances. Predictions are not promises, even when they are made with great confidence.
This idea also applies to business, from forecasting future endeavors to making smart investment choices, and even to planning the rest of your life. We have no idea what the future holds, no matter how ‘good’ our predictions are.
How Personal Biases Influence Our Interpretation of Data
Facts and data can seem objective; however, people often interpret them through the lens of their preconceptions. For example, a fan of a particular team will often list all the great performances by their favorite team, dismissing losses as flukes and giving up quickly. On the other hand, that same fan remembers an opposing team’s misplays for a long time.
It is known as confirmation bias. People place more importance on information that supports their prior belief and largely disregard contradictory information.
Another bias seen in forecasting and statistical analysis is recency bias. This bias can cause one to overvalue recent events or occurrences. For example, a team can win a huge game and suddenly be viewed as so much better than their overall body of work. The reverse can also be true: a team can have a poor game and suddenly have its overall performance viewed in a worse light.
Similarly, people misuse statistics in the business world by using recent sales figures to justify decisions and by giving a company a bad name after one terrible experience with a product or service, while giving a company a good name after one great experience.
We should become more aware of our current thinking and try to change our perspectives to form more objective opinions. Hence, instead of asking whether there is enough evidence to confirm our opinions, we should ask ourselves what evidence would contradict our current views.
Why More Data Doesn’t Automatically Mean Better Decisions
Today’s sports organizations produce a lot of data. Advanced software packages monitor and analyze many variables, such as how a player moves on the pitch, where a shot is taken, and a player’s physical load.
More data does not necessarily equal better decisions, however. Because many sports organizations have taken to collecting and tracking a plethora of different statistics, the data then becomes its own problem. Often, many of the statistics collected are not relevant to the decisions being made.
A football analyst analyzing a forthcoming match might find it more useful to understand a team’s defensive efficiency and player availability than to sift through dozens of statistical indicators of unrelated aspects of performance.
Understanding which information matters to the decision at hand is the key skill in analyzing large amounts of information.
Remember also that although technology can enable better analysis, it will not replace the need to interpret results properly.
Learning to Make Better Decisions When Outcomes Are Unpredictable
It is also important to acknowledge that, despite best efforts, unexpected outcomes can and do occur even with high-quality information.
This still does not mean that the analysis is without value. But it does underscore the need to develop the best possible decision-making process.
By recognizing mistakes, understanding their causes, and adapting accordingly, you can improve your judgment. Judgment improvement techniques also apply outside of sports and are especially useful when dealing with uncertain events.
You can also apply better decision-making in all parts of your life by using lessons learned from sports analytics.
Of course, making good decisions does not require perfect predictions. If you can make good decisions based on what the evidence suggests, you must also acknowledge what you don’t know and be prepared for when things don’t go the way you expected.