A guide to using natural language processing to improve your betting strategies
Thank you for reading this post, don’t forget to subscribe!Prompt engineering is the process of designing and testing natural language prompts that can elicit useful information from a language model. A language model is a computer program that can generate or analyze text based on a large corpus of data. Prompt engineering can help sports bettors leverage the power of language models to gain insights, predictions, and tips for their bets.
What is a language model and how does it work?
A language model is a computer program that can generate or analyze text based on a large corpus of data. The data can be any text from various sources, such as books, articles, blogs, social media, etc. The language model learns the patterns, rules, and probabilities of natural language from the data, and then uses them to produce or interpret new texts.
One of the most popular and advanced language models available today is GPT-3, developed by OpenAI. GPT-3 can generate coherent and diverse texts on almost any topic, given a suitable prompt. A prompt is a text input that triggers the language model to produce a text output. The prompt can be a question, a statement, a keyword, a phrase, or any other text that specifies the task or the goal of the language model.
GPT-3 uses a deep neural network, which is a type of artificial intelligence that mimics the structure and function of the human brain. The neural network consists of multiple layers of units, called neurons, that process and transmit information. The neural network learns from the data by adjusting the connections and weights between the neurons, based on the feedback and the error signals. The more data and feedback the neural network receives, the more accurate and reliable it becomes.
GPT-3 is a generative model, which means that it can create new texts that are not present in the data. It does so by using a technique called self-attention, which allows it to focus on the most relevant parts of the data and the prompt, and to ignore the irrelevant or noisy parts. Self-attention also enables GPT-3 to capture the long-term dependencies and the context of the text, which are essential for generating coherent and meaningful texts.
Why is prompt engineering useful for sports betting?
Prompt engineering is useful for sports betting because it can help you leverage the power of language models to gain insights, predictions, and tips for your bets. Language models can provide you with valuable information that can help you make better and more informed betting decisions, such as:
- The probability of a certain team or player winning a match or a tournament.
- The performance and the strengths and weaknesses of a team or a player.
- The trends and the patterns of a sport or a league.
- The factors and the variables that can affect the outcome of a match or a tournament.
- The opinions and the sentiments of the experts and the public.
– The most likely outcome of a match or a tournament, based on the current standings, the head-to-head records, the recent form, and the injury reports of the teams or players involved.
– The best value bets for a match or a tournament, based on the odds offered by different bookmakers, the expected return, and the risk involved.
– The most influential factors or variables that can affect the performance or the result of a team or a player, such as the weather, the venue, the referee, the motivation, the fatigue, or the tactics.
– The opinions or sentiments of the experts or the public about a match or a tournament, such as the predictions, the analysis, the comments, or the reviews, and how they differ from the actual outcome or the language model’s output.
– The strengths and weaknesses of a team or a player, such as the skills, the attributes, the style, the consistency, or the adaptability, and how they match up against their opponents or the challenges of the sport or the league.
– The trends or patterns of a team or a player, such as the scoring rate, the defensive record, the win-loss ratio, the streaks, or the fluctuations, and how they change over time or across different situations or contexts.
– The similarities or differences between two or more teams or players, such as the history, the achievements, the statistics, the reputation, or the personality, and how they affect their rivalry or their cooperation.
– The anomalies or outliers of a team or a player, such as the unexpected results, the surprising performances, the controversial incidents, or the remarkable feats, and how they can be explained or justified by the language model.
– The historical or cultural aspects of a sport or a league, such as the origins, the rules, the traditions, the legends, or the fans, and how they influence the present or the future of the sport or the league.
– The random or unpredictable events or factors that can occur or influence a sport or a league, such as the luck, the injuries, the suspensions, or the scandals, and how they add or reduce the uncertainty or the fairness of the sport or the league.
However, not all prompts are equally effective in eliciting the desired information from the language model. Some prompts may be too vague, too specific, too biased, or too complex for the language model to handle. Therefore, prompt engineering involves finding the optimal way to phrase your query or statement, as well as providing some relevant context, examples, or constraints to guide the language model.
By using prompt engineering, you can improve the quality and the reliability of the information you get from the language model, as well as the efficiency and the speed of your query or statement. You can also explore the capabilities and the limitations of the language model, as well as discover new and useful information for your betting decisions.
How to use prompt engineering effectively?
Prompt engineering is a trial-and-error process that requires some experimentation and fine-tuning. However, it can also be a rewarding and fun way to explore the capabilities and limitations of language models, as well as to discover new and useful information for your betting decisions. To use prompt engineering effectively, you can follow these steps:
- Define your goal and your question. What information do you want to get from the language model? What is the purpose and the scope of your query or statement? How specific or general do you want the answer or the output to be?
- Choose a suitable language model. What language model are you going to use? What are its features and its limitations? What are its strengths and its weaknesses? How familiar are you with its interface and its functionality?
- Design and test your prompt. How are you going to phrase your query or statement? What context, examples, or constraints are you going to provide to the language model? What keywords or phrases are you going to use to trigger the language model’s memory or attention? How are you going to format and style your prompt and your expected output?
- Evaluate and refine your prompt. How well does the language model respond to your prompt? How accurate and reliable is the information you get from the language model? How relevant and useful is the information for your betting decision? How can you improve your prompt to get better results?
Examples of prompt engineering for different sports and betting scenarios
In this section, we will provide some examples of prompt engineering for different sports and betting scenarios, using GPT-3 as the language model. We will show how to design and test different prompts, as well as how to evaluate and refine them. We will also provide some tips and best practices to follow when using prompt engineering for sports betting.
Example 1: Probability of a team winning a match
One of the most common and basic types of information that sports bettors want to know is the probability of a certain team winning a match. This can help them compare the odds offered by different bookmakers, as well as to assess the risk and the reward of their bets. However, calculating the probability of a team winning a match is not a simple task, as it depends on many factors and variables, such as the form, the injuries, the motivation, the tactics, the weather, the referee, etc. Therefore, using a language model to estimate the probability of a team winning a match can be a useful and convenient tool for sports bettors.
Let’s say we want to know the probability of Liverpool beating Manchester City in the next Premier League game. We can use GPT-3 to generate a numerical answer, along with some reasoning and evidence. Here is an example of a prompt we can use:
Q: What are the chances of Liverpool beating Manchester City in the next Premier League game?
A: The chances of Liverpool beating Manchester City in the next Premier League game are about 33%. This is based on the following factors:
- Liverpool are the defending champions and have a strong record at home, where they have won 23 of their last 24 games.
- Manchester City are the current leaders and have a superior squad, with more depth and quality in every position.
- Liverpool have several key players injured or suspended, such as Van Dijk, Gomez, Henderson, and Mane.
- Manchester City have a better head-to-head record against Liverpool, having won four of the last six meetings.
Therefore, Manchester City are the favorites to win the game, but Liverpool still have a chance to cause an upset, especially if they can exploit Manchester City’s defensive weaknesses and use their high-pressing and counter-attacking style.
This is a fairly good prompt and answer, as it provides a clear and concise question, a numerical and percentage answer, and some relevant and logical factors that support the answer. However, it can be improved by using some of the prompt engineering techniques we mentioned earlier, such as:
- Providing some background information or facts that can help the language model understand the topic and the task. For example, we can add some information about the date, the venue, the standings, and the recent form of the teams.
- Using examples or templates to show the language model the format and style of the expected output. For example, we can add some examples of similar questions and answers, or use a template that specifies the structure and the components of the answer.
- Using keywords or phrases that can trigger the language model’s memory or attention to the relevant data. For example, we can use words or terms that are specific to the sport, the league, or the teams, such as goals, points, matches, etc.
- Using filters or rules to limit the scope or range of the possible outputs. For example, we can specify the minimum and the maximum values for the probability, or the number and the length of the factors.
Here is an example of an improved prompt, using some of these techniques:
Q: What are the chances of Liverpool beating Manchester City in the next Premier League game, which will be played on February 7th, 2021, at Anfield Stadium? Liverpool are currently fourth in the table, with 40 points from 21 games, while Manchester City are first, with 47 points from 20 games. Liverpool have won three, drawn one, and lost two of their last six games, while Manchester City have won all of their last six games.
A: The chances of Liverpool beating Manchester City in the next Premier League game are about 28%. This is based on the following three factors:
- Liverpool have a strong record at home, where they have won 23 of their last 24 games, scoring 67 goals and conceding only 17.
- Manchester City have a superior squad, with more depth and quality in every position, and have scored the most goals (37) and conceded the fewest (13) in the league.
- Liverpool have several key players injured or suspended, such as Van Dijk, Gomez, Henderson, and Mane, while Manchester City have a fully fit and available squad.
Therefore, Manchester City are the favorites to win the game, but Liverpool still have a chance to cause an upset, especially if they can exploit Manchester City’s defensive weaknesses and use their high-pressing and counter-attacking style.
This is a better prompt and answer, as it provides more context and details, a more consistent and structured format, and more specific and accurate data. It also uses a lower probability for Liverpool, which reflects the current situation and the form of the teams more realistically.
Example 2: Performance and strengths and weaknesses of a team or a player
Another type of information that sports bettors want to know is the performance and the strengths and weaknesses of a team or a player. This can help them analyze the match-ups, the styles, the tactics, and the strategies of the teams or the players, as well as to identify the opportunities and the threats for their bets. However, evaluating the performance and the strengths and weaknesses of a team or a player is not an easy task, as it involves a lot of data, statistics, metrics, and subjective judgments. Therefore, using a language model to assess the performance and the strengths and weaknesses of a team or a player can be a helpful and convenient tool for sports bettors.
Let’s say we want to know the performance and the strengths and weaknesses of Novak Djokovic, the world number one tennis player. We can use GPT-3 to generate a text output that summarizes and compares his performance and his strengths and weaknesses, based on his recent results, his rankings, his records, and his skills. Here is an example of a prompt we can use:
Q: How would you describe the performance and the strengths and weaknesses of Novak Djokovic, the world number one tennis player?
A: Novak Djokovic is the world number one tennis player, and one of the greatest players of all time. He has won 17 Grand Slam titles, 36 Masters titles, and five ATP Finals titles, among many other trophies. He has been ranked number one for a total of 304 weeks, and is the only player to hold all four Grand Slam titles on three different surfaces at the same time. He has also won a record eight Australian Open titles, and is the reigning champion of the tournament.
Djokovic’s performance is remarkable, as he is consistent, dominant, and versatile. He has a high winning percentage of 83.4%, and has beaten every other player in the top 10 at least once. He has also reached the final of every Grand Slam tournament at least five times, and has won at least one Grand Slam title every year since 2011, except for 2017. He has also won at least two Masters titles every year since 2013, except for 2017 and 2020.
Djokovic’s strengths are his exceptional baseline game, his mental toughness, and his physical fitness. He has a powerful and accurate serve, a solid and consistent forehand, and a lethal and versatile backhand. He can hit winners from any angle and position, and can also defend and counter-attack with ease. He is also very agile and flexible, and can cover the court and reach any ball. He is also very focused and determined, and can handle pressure and adversity well. He is also very resilient and durable, and can play long and intense matches without losing stamina or quality.
Djokovic’s weaknesses are his vulnerability to drop shots, his occasional loss of concentration, and his emotional instability. He sometimes struggles to deal with drop shots, as he has to run forward and bend low, which can disrupt his rhythm and balance. He sometimes also loses his concentration and makes unforced errors, especially when he is leading or facing a weaker opponent. He sometimes also loses his temper and gets frustrated, which can affect his performance and his behavior. He has been known to smash his racket, argue with the umpire, or yell at his team or the crowd.
Therefore, Djokovic is a phenomenal and formidable player, who has very few flaws and many advantages. He is the favorite to win any match or tournament he plays, but he is not invincible or unbeatable. He can be challenged and defeated by players who can exploit his weaknesses and counter his strengths, as well as by players who can match his level and intensity.
This is a fairly good prompt and answer, as it provides a clear and concise question, a comprehensive and balanced answer, and some relevant and factual information. However, it can be improved by using some of the prompt engineering techniques we mentioned earlier, such as:
Method | Description |
Providing background information or facts | Helps the language model understand the topic and the task by adding information about the sport, the ranking system, the tournaments, and the players. |
Using examples or templates | Shows the language model the format and style of the expected output by adding examples of similar questions and answers or using a template. |
Using keywords or phrases | Triggers the language model’s memory or attention to the relevant data by using words or terms specific to the sport, the player, or the skills. |
Using filters or rules | Limits the scope or range of the possible outputs by specifying the number and length of the paragraphs or the number and type of strengths and weaknesses. |
Using questions or prompts | Guides the language model to generate specific and relevant content by asking it to name strengths and weaknesses or compare performance with other players. |
Using feedback or reinforcement | Rewards or penalizes the language model for its output quality by using a scoring system or reward function that evaluates accuracy, coherence, and completeness. |
Using data augmentation or paraphrasing | Increases the diversity and robustness of the input and output by using synonyms, rephrasing, or reordering to create different versions of the same question or answer. |
Using references or sources | Supports or verifies the information in the output by providing links or citations to reliable websites or articles containing factual data or statistics. |
Using segmentation or chunking | Breaks down the task or output into smaller and manageable parts by dividing the answer into paragraphs, each focusing on one aspect of performance or strengths and weaknesses. |
Using summarization or simplification | Reduces the complexity or length of the output by using bullet points, tables, or graphs to summarize or visualize the main points or data, or by using simple and concise language. |
Here is an example of an improved prompt, using some of these techniques:
Q: How would you describe the performance and the strengths and weaknesses of Novak Djokovic, the world number one tennis player, in four paragraphs? Tennis is a racket sport that is played on different surfaces, such as hard, clay, or grass. The ranking system is based on the points earned from the results of the tournaments, which are categorized into four levels: Grand Slams, Masters, ATP Finals, and others. Djokovic is a Serbian player who has been playing professionally since 2003, and has been ranked number one since 2011, except for a few periods.
A: Novak Djokovic is the world number one tennis player, and one of the greatest players of all time. He has won 17 Grand Slam titles, 36 Masters titles, and five ATP Finals titles, among many other trophies. He has been ranked number one for a total of 304 weeks, and is the only player to hold all four Grand Slam titles on three different surfaces at the same time. He has also won a record eight Australian Open titles, and is the reigning champion of the tournament.
Djokovic’s performance is remarkable, as he is consistent, dominant, and versatile. He has a high winning percentage of 83.4%, and has beaten every other player in the top 10 at least once. He has also reached the final of every Grand Slam tournament at least five times, and has won at least one Grand Slam title every year since 2011, except for 2017. He has also won at least two Masters titles every year since 2013, except for 2017 and 2020.
Djokovic’s main strengths are his exceptional baseline game, his mental toughness, and his physical fitness. He has a powerful and accurate serve, a solid and consistent forehand, and a lethal and versatile backhand. He can hit winners from any angle and position, and can also defend and counter-attack with ease. He is also very agile and flexible, and can cover the court and reach any ball. He is also very focused and determined, and can handle pressure and adversity well. He is also very resilient and durable, and can play long and intense matches without losing stamina or quality.
Djokovic’s main weaknesses are his vulnerability to drop shots, his occasional loss of concentration, and his emotional instability. He sometimes struggles to deal with drop shots, as he has to run forward and bend low, which can disrupt his rhythm and balance. He sometimes also loses his concentration and makes unforced errors, especially when he is leading or facing a weaker opponent. He sometimes also loses his temper and gets frustrated, which can affect his performance and his behavior. He has been known to smash his racket, argue with the umpire, or yell at his team or the crowd.
This is a better prompt and answer, as it provides more context and details, a more consistent and structured format, and more specific and accurate information. It also uses a more concise and precise language, and avoids repetition and redundancy.
Example 3: Trends and patterns of a sport or a league
Another type of information that sports bettors want to know is the trends and patterns of a sport or a league. This can help them understand the dynamics, the changes, and the evolution of the sport or the league, as well as to predict the future outcomes and the opportunities for their bets. However, identifying and analyzing the trends and patterns of a sport or a league is not a trivial task, as it involves a lot of data, statistics, metrics, and visualization. Therefore, using a language model to detect and describe the trends and patterns of a sport or a league can be a useful and convenient tool for sports bettors.
Let’s say we want to know the trends and patterns of the NBA, the professional basketball league in the United States. We can use GPT-3 to generate a text output that summarizes and compares the trends and patterns of the NBA, based on its history, its teams, its players, and its rules. Here is an example of a prompt we can use:
Q: What are the main trends and patterns of the NBA?
A: The NBA is the professional basketball league in the United States, and one of the most popular and competitive sports leagues in the world. It consists of 30 teams, divided into two conferences: the Eastern Conference and the Western Conference. Each team plays 82 regular season games, followed by a playoff tournament, where the best eight teams from each conference compete for the championship. The NBA was founded in 1946, and has undergone many changes and developments since then.
Some of the main trends and patterns of the NBA are:
- The dominance of a few teams and players. The NBA has been characterized by the emergence and the dominance of a few teams and players, who have won multiple championships and awards, and have set many records and milestones. For example, the Boston Celtics and the Los Angeles Lakers have won 17 and 16 titles, respectively, and have featured some of the greatest players of all time, such as Bill Russell, Larry Bird, Magic Johnson, and Kobe Bryant. Other dominant teams include the Chicago Bulls, who won six titles in the 1990s, led by Michael Jordan, the San Antonio Spurs, who won five titles between 1999 and 2014, led by Tim Duncan, and the Golden State Warriors, who won three titles between 2015 and 2018, led by Stephen Curry. Some of the dominant players include Kareem Abdul-Jabbar, who holds the record for the most points scored in the NBA history, LeBron James, who has won four titles and four MVP awards, and Giannis Antetokounmpo, who has won two consecutive MVP awards.
- The evolution of the game style and the rules. The NBA has also been characterized by the evolution and the innovation of the game style and the rules, which have influenced and reflected the trends and the preferences of the players, the coaches, the fans, and the media. For example, the NBA has seen the rise and the fall of different game styles, such as the fast-paced and high-scoring style of the 1960s and 1970s, the physical and defensive style of the 1980s and 1990s, and the skill-based and three-point oriented style of the 2000s and 2010s. The NBA has also seen the introduction and the modification of different rules, such as the shot clock, the three-point line, the salary cap, the draft lottery, and the replay review, which have affected and shaped the game and the competition.
- The diversity and the globalization of the league. The NBA has also been characterized by the diversity and the globalization of the league, which have enhanced and enriched the culture and the identity of the sport and the organization. For example, the NBA has seen the increase and the integration of players from different racial, ethnic, and national backgrounds, such as African Americans, Latinos, Asians, Europeans, and Africans, who have brought their talents, skills, and perspectives to the game. The NBA has also seen the expansion and the popularity of the league in different regions and markets, such as Canada, China, India, and Africa, who have become avid and loyal fans and supporters of the sport.
Therefore, the NBA is a dynamic and complex league, that has experienced and exhibited many trends and patterns, that have influenced and defined the history, the present, and the future of the sport.
This is a fairly good prompt and answer, as it provides a clear and concise question, a comprehensive and coherent answer, and some relevant and factual information. However, it can be improved by using some of the prompt engineering techniques we mentioned earlier, such as:
- Providing some background information or facts that can help the language model understand the topic and the task. For example, we can add some information about the sport, the league, the teams, and the players.
- Using examples or templates to show the language model the format and style of the expected output. For example, we can add some examples of similar questions and answers, or use a template that specifies the structure and the components of the answer.
- Using keywords or phrases that can trigger the language model’s memory or attention to the relevant data. For example, we can use words or terms that are specific to the sport, the league, or the trends, such as points, rebounds, assists, etc.
- Using filters or rules to limit the scope or range of the possible outputs. For example, we can specify the number and the length of the paragraphs, or the number and the type of the trends and patterns.
Here is an example of an improved prompt, using some of these techniques:
Q: What are the main trends and patterns of the NBA, in three paragraphs? The NBA is the professional basketball league in the United States, and one of the most popular and competitive sports leagues in the world. It consists of 30 teams, divided into two conferences: the Eastern Conference and the Western Conference. Each team plays 82 regular season games, followed by a playoff tournament, where the best eight teams from each conference compete for the championship. The NBA was founded in 1946, and has undergone many changes