Prompt Engineering Your Own Betting Analysis: What LLMs Are Actually Useful For

[Betting Forum]

Large language models are the most overhyped and most underused tools in the betting analyst's toolkit simultaneously. Overhyped because the initial reaction to something that can produce fluent, confident-sounding text about any topic is to assume it knows things. Underused because once bettors discover it doesn't reliably know specific facts - that it will invent a statistic with the same syntactic confidence as a real one - they dismiss it entirely and return to their previous workflow.

Both reactions miss the actual shape of what these tools are good at. A large language model is not a database. It is not a search engine. It is not an authority on specific numerical facts about football matches. Treating it as any of these things produces the hallucinated statistics problem that correctly makes people distrust it.

What a large language model is, genuinely and usefully, is a sophisticated text processor with broad general knowledge, excellent reasoning capability, and remarkable flexibility in transforming information from one format to another. When used for tasks that match these actual capabilities - and specifically when the information it's working with is provided by you rather than generated from its own memory - it produces outputs that meaningfully improve a betting research workflow. The distinction between providing information and asking for information is the central principle that separates productive from unproductive LLM use in betting analysis.

This guide is about the specific tasks where LLMs add genuine value, the specific prompting approaches that produce useful outputs for those tasks, and the specific situations where the model will confidently mislead you if you let it.

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The Hallucination Problem: Understanding It Precisely​

Before the use cases, the failure mode needs to be understood precisely rather than vaguely. Vague distrust - "it makes things up sometimes" - leads to either excessive reliance or total avoidance. Precise understanding of when and why it produces false information allows selective use that avoids the failure mode.

Large language models predict the next token in a sequence based on patterns learned from enormous amounts of text. They don't retrieve facts from a verified database. They generate text that is statistically consistent with patterns in their training data. When asked a question that has a specific numerical answer - "what was Manchester City's xG against in the 2022-23 season?" - the model generates an answer that looks like the kind of answer that question produces. The generated answer is statistically plausible-looking. It is not necessarily correct.

The hallucination problem is most severe for specific numerical claims, specific event dates, specific match results, specific player statistics, and specific research findings with precise figures. These are the categories where the model generates a confident-looking number that may bear no relationship to the actual figure. The more specific the factual claim, the higher the hallucination risk.

The hallucination problem is least severe for reasoning processes, conceptual frameworks, analytical approaches, and general knowledge about well-documented topics. The model's reasoning about why a specific type of tactical situation tends to produce a specific type of match script is not a hallucination risk in the same way - it's applying general football knowledge in a reasoning process rather than retrieving a specific fact.

The practical rule: never ask an LLM for specific numerical facts about football that you haven't already verified from a primary source. Always ask an LLM to reason about, structure, compare, or interpret information that you provide. The model working with your information is reliable. The model generating its own information is risky.

What LLMs Are Genuinely Useful For​

Summarising and Synthesising Large Text Volumes​

A pre-match press conference transcript runs to fifteen hundred words. A manager who holds press conferences twice a week produces three thousand words of relevant text per week. Reading and processing this volume across multiple clubs in multiple competitions is time-consuming in a way that reduces how many press conferences actually get read.

Paste the press conference transcript into an LLM and ask it to extract the specific information relevant to your betting analysis - injury disclosures, tactical hints, squad availability signals, team selection indications. The model will process the full transcript and identify the passages most relevant to your specified focus in thirty seconds. The output won't miss the specific throwaway line about a player training away from the main group that's buried in paragraph fourteen.

This is text processing in service of your analysis, not fact generation. The model is working with a document you've provided. The information it extracts is from that document - it can't hallucinate a fact that isn't there, and if you spot something the extraction missed, you can ask it to look again. This is the use case where LLMs add most unambiguous value relative to the time it saves.

The same approach applies to: injury reports across multiple club sources, post-match manager interviews, analyst reports you want key points extracted from, referee profile information from sport journalism, and any other large volume of text that contains relevant betting information distributed across a lot of non-relevant content.

The prompting approach that works: be specific about what you want extracted. "Summarise this press conference" produces a general summary. "Extract from this press conference: any information about squad availability for Saturday, any tactical signals about formation or system, and any language suggesting concern about the team's current form" produces targeted extraction. Specific output requirements produce more useful outputs than general requests.

Generating Structured Comparison Frameworks​

You want to compare two teams' tactical approaches before a fixture. You have a general sense of both systems but you want a structured framework for thinking through the matchup - which dimensions to consider, which to weight more heavily, what the likely interaction between their styles will produce.

The LLM doesn't know this season's specific numbers. But it can generate an excellent framework for thinking through the tactical comparison if you describe both teams' approaches. The output is a structured set of dimensions - pressing intensity interaction, defensive shape vulnerability, set piece dimension, transition quality - that you then populate with your own data and knowledge.

This is using the model as a thinking partner for framework generation rather than as a fact source. The framework it generates organises your analysis rather than doing your analysis. You fill in the actual data. The structure it produces makes sure you're not missing dimensions you'd otherwise overlook.

Works particularly well for: pre-match tactical matchup frameworks, multi-variable comparison structures for squad assessment, scenario analysis frameworks for final day arithmetic, and any situation where you want to make sure you're thinking about a problem systematically rather than missing variables.

Prompting approach: describe the specific problem you're trying to structure. "I'm comparing two teams for a second-leg European playoff. Team A holds a two-goal aggregate lead and plays a high defensive block. Team B needs to attack. Help me build a framework for assessing how this match will play out that covers all the relevant dimensions." The more context you provide, the more tailored and useful the framework output.

Scenario Analysis and Decision Trees​

Final day survival arithmetic produces a specific set of scenarios where specific combinations of results produce specific outcomes. Working through every branch of a multi-game, multi-team final day scenario manually is error-prone and time-consuming. An LLM can hold a complex multi-conditional scenario tree in context and help you work through all the branches systematically.

"Team A needs to win, and needs Team B to drop points. Team B is playing Team C who needs a draw to stay up. Team D is also in the survival mix and is playing Team E. Walk me through all the result combinations and what each produces for each of these teams."

The model will systematically work through the branches, identify the combinations you need to focus on, and flag the scenarios you might have missed. You should verify the arithmetic separately - the model can make logical errors in complex multi-step calculations - but the scenario enumeration is reliable and saves the mental overhead of holding all the branches simultaneously.

The same approach applies to: Asian Handicap quarter-ball cross-line consistency checks where you're working through implied probabilities across multiple markets, two-legged playoff arithmetic across different first-leg result scenarios, and any situation where you want to systematically enumerate the consequences of different outcomes before they occur.

Reasoning About Mechanism​

You want to think through why a specific variable - say, set piece specialist absence - affects match outcomes through what mechanism, and whether you're reasoning about it correctly. Writing out your reasoning and asking the model to challenge it, extend it, or identify gaps produces a genuine analytical dialogue that improves the reasoning.

"Here's my reasoning about why set piece specialist absence affects total goals markets. The specialist's delivery quality generates higher-xG set piece opportunities than a replacement would. Over a match with, say, ten corners and four direct free kicks in dangerous positions, the replacement's lower-quality delivery reduces the team's expected set piece xG by roughly 0.15 to 0.25 per match. The market doesn't price this because delivery attribution isn't in standard data feeds. Have I missed any dimensions of this mechanism, or is my reasoning structurally flawed?"

The model will engage with the reasoning, challenge the parts that are weak, extend the parts that could be developed further, and suggest dimensions you might not have considered. This is not the model telling you facts about set piece statistics - it's the model functioning as an analytical discussion partner who helps you reason more rigorously.

The prompting approach: write out your full reasoning first, then ask for critique and extension rather than asking the model to reason from scratch. Providing your reasoning gives the model something specific to engage with. Asking it to generate reasoning from scratch risks the model producing plausible-sounding analysis that isn't tethered to your actual analytical approach.

Writing and Communication​

The most underrated application for bettors who produce any kind of written output - forum posts, research notes, betting journals, content for communities. A large language model is an excellent writing assistant for transforming rough analytical notes into clear, readable prose.

Analytical thinking and clear writing are different skills. Many people who think rigorously about betting analysis write about it in ways that are hard to follow. Rough analytical notes - "xT high, xG low, conversion issue, market anchoring to bad results, similar to promoted team pattern from article" - can be transformed into clear, readable analysis with specific prompting.

This is an area where the model adding its own words is acceptable because writing assistance isn't fact generation. The model organises your ideas into readable prose. Your ideas remain the substance. The model provides structure and clarity. The result is better communication of your actual analysis rather than the model's invented analysis.

What LLMs Are Not Useful For​

Being equally specific about the failure cases is more important than the success cases, because the failure cases carry financial risk.

Specific Statistical Queries​

"What is Burnley's xG against per 90 this season?" Do not ask this. The model will answer with a specific number that sounds right. That number has an unknown relationship to the actual figure. It might be correct - the model has seen a lot of football statistics in its training data - but it might be confidently wrong. You cannot tell which without verifying against a primary source. If you're going to verify against a primary source anyway, skip the model and go directly to the source.

The same applies to: match results from specific dates, historical league table positions, transfer fees, contract lengths, referee appointment records, and any other specific numerical fact about football that exists in a database somewhere. The model is not the database. Use the database.

Injury Confirmation and Team News​

An LLM's training data has a cutoff date. It doesn't know what happened last Thursday. Asking it whether a specific player is injured or available for Saturday is asking it to retrieve current information it doesn't have. It will either correctly state that it doesn't have current information, or - in some implementations - will generate a plausible-sounding but fabricated injury status. Neither outcome is useful. Use club official sources, journalist social media, and the Thursday-to-Saturday monitoring workflow from the line opening article.

Generating Analysis From Scratch Without Provided Data​

"Analyse Manchester City's defensive weaknesses for their upcoming match against Arsenal." This prompt asks the model to generate analysis from its own knowledge of these teams. The resulting analysis will be fluent, structured, and may contain specific-sounding claims about defensive metrics, pressing patterns, and tactical vulnerabilities. Some of it will be accurate. Some will be outdated. Some will be invented. You cannot reliably distinguish which is which without independent verification, which defeats the purpose of asking.

The failure mode is specifically the confident presentation of invented or outdated analysis as if it were current and verified. The model doesn't hedge appropriately in its phrasing - it produces text that reads like well-informed analysis regardless of whether it is. The fluency is constant. The accuracy varies.

The fix: provide the data and ask the model to analyse it. "Here are Manchester City's last eight defensive statistics from FBref: [paste data]. Here is their upcoming fixture context: [describe]. Analyse the specific defensive patterns visible in this data and what they suggest for the upcoming match." Now the model is working with verified information you've provided and cannot hallucinate the specific numbers because they're in the prompt.

Verification of Your Own Analysis​

"Does my analysis of this match make sense?" is a dangerous question because the model will usually say yes, or say yes with minor caveats, because its training produces agreeable responses to analysis it can't independently verify. It doesn't have access to the actual match data to check whether your analysis is correct. It can check whether your reasoning is logically consistent, which is useful. It cannot check whether your premises are factually accurate, which is where your analysis could be wrong in the most consequential ways.

Use the model to check logical consistency of your reasoning. Don't use it to verify factual accuracy of your premises.

Specific Prompting Approaches That Work​

The difference between useful and useless LLM output is often in the prompt structure rather than the task itself. These prompting patterns consistently produce better outputs across the use cases described above.

Paste first, then ask. Always provide the source material before the question. "Here is a press conference transcript: [transcript]. Now, extract all injury and availability information." The transcript in context means the model works from your verified source rather than its memory.

Specify the output format. "Extract the following as a bulleted list with each point under 30 words" produces a usable output. "Summarise the key points" produces something harder to immediately act on. The more specific the format request, the more usable the output.

Ask for reasoning, not conclusions. "What should I bet on in this match?" produces a hallucination-risk output. "Walk me through the analytical considerations for this match given the information I've provided" produces a reasoning process that you then evaluate. Conclusions from the model carry the model's errors. Reasoning processes you evaluate are subjected to your verification.

Provide your own reasoning before asking for critique. "Here is my analysis: [analysis]. What dimensions have I missed, what reasoning is weakest, and what counter-arguments should I consider?" is far more useful than "Analyse this match." The critique of your own reasoning is tethered to your reasoning. The generation of independent reasoning is untethered from reality.

Ask for frameworks, not facts. "What dimensions should I consider when assessing a caretaker appointment?" produces a useful framework output with low hallucination risk. "What was the result of the match when Guardiola was first appointed caretaker?" produces a specific claim with high hallucination risk. Frameworks are within the model's genuine capabilities. Specific historical facts are outside them.

Break complex tasks into steps. "First, extract the injury information from this transcript. Then, given that information, help me think through the implications for the total goals market in Saturday's fixture." Multi-step prompts allow you to verify the output of each step before the model proceeds to the next. An error in the extraction step is visible before it propagates into the implication step.

The Model as a Thinking Partner, Not an Oracle​

The productive mental model for LLM use in betting analysis is dialogue partner rather than authority. A good dialogue partner helps you think more rigorously, challenges your assumptions, extends your framework to dimensions you've missed, and structures your thinking more clearly. They don't replace your thinking or provide facts you haven't verified.

The oracle mental model - asking the model questions and accepting its answers - produces the hallucination problem in its most dangerous form. An oracle that occasionally invents confident-sounding false answers while producing correct ones most of the time is specifically dangerous, because the confident presentation makes the false answers indistinguishable from the correct ones.

The dialogue partner mental model produces genuine analytical improvement. You do the research, gather the data, and form your initial analysis. The model helps you think about it more rigorously, structure it more clearly, identify gaps you've missed, and communicate it more effectively. The substance is yours. The model improves the process.

Anyway. The bettors who will get the most from these tools over the next few years are the ones who resist both the temptation to treat them as oracles and the temptation to dismiss them as useless once the oracle expectation fails. They're powerful text processors with genuine reasoning capability and a specific and serious failure mode around factual invention. Working with their actual capabilities rather than imagined ones is what separates useful adoption from both hype and disappointment.

FAQ​

Q1: Are some large language models significantly more reliable than others for betting analysis purposes, and is it worth paying for access to better models?
Yes to both. The models available as of early 2025 vary meaningfully in their reasoning quality and their tendency to hallucinate specific facts confidently rather than acknowledging uncertainty. The better-performing models - the frontier models from the major AI labs - are more likely to say "I don't have reliable information about that specific figure, you should verify this" rather than generating a confident false answer. They're also better at the reasoning and framework-generation tasks where models genuinely add value. The free-tier models available from various providers are adequate for simple text processing tasks - press conference extraction, basic framework generation - but less reliable for complex multi-step reasoning where errors compound. For a bettor using these tools as a regular part of their workflow, the subscription cost for frontier model access is small relative to the improvement in output quality. The practical test: give both a complex multi-step scenario analysis task with specific numbers provided and evaluate which output is more rigorous and more appropriately caveated.

Q2: Is there a risk that using LLMs in betting analysis creates a homogenisation problem, where many bettors using the same tool arrive at the same analytical conclusions and the edge disappears?
The homogenisation risk is real but more limited than it might appear. The model's output is only as distinctive as the input. Two bettors who ask the same generic question about a match get similar generic outputs. Two bettors who bring their own research, their own data, and their own specific analytical framework to the model as input get outputs shaped by their individual analysis rather than by the model alone. The model is a tool that amplifies what you bring to it rather than a substitute for bringing something. The distinctive edge comes from the quality and specificity of your input - your match knowledge, your tactical observation, your data collection - not from the model itself. Bettors who use LLMs as a substitute for their own research will converge on similar generic outputs. Bettors who use LLMs to amplify their distinctive research will produce distinctive outputs. The distinction is the same one that separates the colour of information taxonomy's amber information from red information - the model processing your amber information improves your workflow. The model generating its own red information produces nothing you couldn't get from reading any generic betting content.

Q3: How should betting analysis prompts handle the model's training data cutoff - the fact that it doesn't know about events after a specific date - and is there a way to work around this limitation?
The cutoff limitation is best worked around by treating the model as a processing tool for current information you provide rather than a source of current information. The cutoff problem only manifests when you ask the model for current information from its memory. When you provide current information in the prompt - pasting the current league table, the current injury list, the current press conference transcript - the model works with current data without needing to retrieve it from its memory. The practical workflow: gather current data from primary sources, paste it into the prompt, ask the model to process and reason about what you've provided. The model's knowledge of football in general - how tactical systems work, what specific variables tend to predict - doesn't expire with the cutoff in the same way as specific current facts. Its general football knowledge is a stable asset. Its specific current factual knowledge is a liability. Build workflows that use the asset and avoid the liability.