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That's the actual workflow problem. Not injury tracking in the abstract - the specific chaos of aggregating partially reliable information from multiple sources under time pressure, on multiple fixtures simultaneously, when the value of acting quickly is in direct conflict with the value of acting accurately.
An LLM doesn't solve the time problem. It doesn't get you information faster than a journalist with a training ground contact. What it does is process and weight information faster than you can, consistently, without the selective attention that causes you to overweight the source you happened to check first. Used correctly, it converts the chaos of Friday afternoon into a structured summary with explicit confidence levels in about ten minutes. That's the workflow this article describes.
Why a Flat List of Reports Is Useless
The natural output of manual team news aggregation is a list. Player X is a doubt according to Source A. Player X is expected to start according to Source B. Player Y missed training according to Source C. Manager confirmed Player Z is fit in press conference. And so on.
A flat list has no decision architecture. It gives the same visual weight to a journalist with a reliable track record and a fan account speculating from a grainy training ground photo. When you're in a hurry - which is always, on Friday afternoon - you instinctively anchor to whichever item you read last, or whichever one confirms what you already suspected. That's not analysis. That's noise with an analytical veneer.
What you actually need is a confidence-weighted summary. Not "here are the reports" but "here is the current most probable picture of this squad's availability, here is the confidence level for each element of that picture, here is the specific source basis for each confidence level, and here is what would change the assessment if new information arrives before kick-off."
That structure requires evaluating sources against each other and against your historical calibration of their reliability. Doing it manually for three fixtures on Friday afternoon is genuinely difficult. Giving an LLM the raw reports and a calibration framework and asking it to produce the structure takes ten minutes.
Building Your Source Reliability Calibration
This is the prerequisite that most people skip, which is why most people's team news analysis is worse than it should be.
Every source you use regularly for team news has a track record. Some are more reliable than others in ways that are consistent across time and mostly predictable. A club's official Twitter account is reliable for confirmed absences and confirmed returns - they don't announce fitness information they're not certain about - but useless for doubt-status information they have no incentive to release early. A manager's press conference is reliable for information the manager wants you to have and unreliable for information he's deliberately obscuring. A beat journalist with a specific club produces better reliability on their covered club than a general football journalist does, but may have an incentive to break news before it's confirmed. Fan accounts aggregating training ground observations are the lowest base reliability but the highest potential ceiling - when the information is right, it's often the earliest available.
Write this down. Literally. A simple reference document with each source you use regularly, their reliability tier (high, medium, low, directional), the specific type of information they're reliable for, and any known biases. It doesn't need to be long. Ten sources, a sentence each. The act of writing it forces you to be explicit about something you currently hold as a vague impression.
Once that document exists, you give it to the LLM as context before asking it to produce a team news summary. That's the specific piece of the workflow most people omit, and it's the piece that makes the confidence weighting actually reflect your calibration rather than the model's generic assessment of source credibility.
The Source Aggregation Step
Before you run any prompt, you need to collect the raw material. The collection step has its own discipline.
For each fixture you're monitoring, create a simple text document - or just a section of a running document - and paste or type the relevant reports as you find them. Include the source name, the approximate time of the report, and the exact claim being made. Do not paraphrase. Do not summarise. Do not editorially filter at this stage. The filtering happens in the prompt, not in the collection.
The typical sources for a weekend Premier League fixture, in order of when they become available: club injury updates released earlier in the week, Thursday manager press conferences, Friday training ground reports from beat journalists, Friday manager press conference if held, late Friday or Saturday morning club announcements for confirmed late withdrawals.
For Championship and below the timing shifts - press conferences are often Thursday or Friday, beat journalist coverage is thinner, and club official communications are less consistent. The collection step is the same but the source universe is smaller and the average reliability is lower. Do not apply Premier League calibrations to Championship sources. They are different information environments.
The collection document for a single fixture might look like this. Source: Club official Twitter, Friday 9am, confirmed Player X will miss through illness. Source: Beat journalist A, Thursday 6pm, manager said Player Y is "touch and go" for Saturday. Source: Journalist B known for early team news, Friday 11am, understands Player Y trained fully on Friday and is expected to start. Source: Manager press conference transcript, Thursday, manager described Player Z as available and fully fit. Source: Fan account aggregating training ground observation, Friday 1pm, Player Y was seen doing full training but Player Z appeared to leave early.
Five sources, potentially conflicting, each with different reliability implications. That's a collection document. Now you run the prompt.
The Core Workflow Prompt
Give the LLM your source reliability calibration document first, before the collection document. Frame it explicitly:
"The following is my personal source reliability calibration for team news. I want you to use this to weight the sources in the team news collection I'll give you next. [Paste calibration document.] Understood? Now here is the team news collection for [Fixture, Date]: [Paste collection document.] Using my source reliability calibration, produce a confidence-weighted team news summary with the following structure. First, for each player mentioned across any source, give me: their current most probable availability status, your confidence level in that status expressed as high / medium / low / unclear, the specific sources that support it, and any source conflicts that affect confidence. Second, identify the single most important unresolved uncertainty - the player whose availability is most unclear and whose status would most change my assessment of the fixture. Third, tell me what information would resolve the key uncertainty if it arrives before kick-off."
That three-part structure does specific things. The per-player section converts the flat list into a structured assessment. The single most important uncertainty section forces prioritisation - you don't have equal analytical attention for every doubt, and naming the one that matters most focuses your monitoring. The information trigger section converts the summary from a static picture into an active monitoring framework - you know exactly what to look for in the final hours before the line moves.
One instruction to always include: "Where two sources conflict directly, describe the conflict explicitly rather than averaging them. I want to see the disagreement, not a smoothed assessment that obscures it." Source conflicts are informative. A manager saying a player is available while a beat journalist reports a training absence is a signal - either the manager is managing information or the journalist is wrong - and both possibilities affect how you should interpret subsequent information. Hiding the conflict behind a medium confidence rating loses that signal entirely.
Calibrating the Confidence Levels
The confidence levels in the summary are only as good as your source calibration. This is worth being honest about. The first few times you run this workflow, your calibration will be rough - you'll have general impressions of source reliability rather than tracked accuracy records. The output will reflect that roughness.
The calibration improves over time if you close the loop. After each set of fixtures you've monitored, note which confidence assessments were right and which were wrong. Not in exhaustive detail - just a quick check: high confidence calls that turned out incorrect, low confidence calls that resolved cleanly. The accumulating pattern tells you whether specific sources are better or worse than your calibration assumed.
Some patterns are consistent enough to encode immediately without personal tracking. Official club communications are almost always accurate for confirmed absences and almost always silent on doubt-status players. Manager press conference statements about specific players are reliable when the information has no strategic value to obscure and unreliable when there's a tactical motivation to mislead. Beat journalists are reliable in the direction of their incentives - they break good news faster than bad news because good news builds relationships with clubs and bad news burns them. All of these are starting calibrations. Your personal tracking refines them over time.
The confidence level for a player status should also reflect the time remaining until kick-off. A medium confidence assessment on Thursday afternoon might be a high confidence assessment by Saturday morning if no contradicting information has arrived in the interim. Add a simple instruction to the prompt: "Note where confidence levels might change with the passage of time if no new information arrives - specifically, identify any current medium confidence assessments that would become high confidence by Saturday morning under a no-new-information assumption."
The Multi-Fixture Version
On a typical Saturday with three or four fixtures you're actively monitoring, running the single-fixture prompt individually for each is manageable but leaves value on the table. There's a better second step for the multi-fixture case.
Run the single-fixture prompts individually and collect the summaries. Then run a second prompt across all the summaries:
"The following are confidence-weighted team news summaries for [number] fixtures I'm monitoring this weekend. I want you to rank these fixtures by the level of remaining uncertainty in their team news pictures - specifically, which fixture has the most unresolved meaningful uncertainty that could shift my analysis, and which has the most settled picture. For each fixture, note whether the unresolved uncertainties affect specific markets differently - a key striker doubt affects total goals and Asian Handicap lines differently than a goalkeeper doubt does. Do not make betting recommendations. Just identify which fixtures have the most analytically consequential unresolved uncertainty and in which specific markets."
That second prompt converts individual fixture summaries into a monitoring priority list. You know which fixture needs the most attention in the hours before kick-off, and you know which specific markets are most affected by the remaining uncertainties. It's the difference between monitoring everything equally and monitoring intelligently.
The Line Movement Integration
Team news monitoring is only useful in relation to whether the line has already moved. A high-confidence assessment that a key player is absent is valuable information if it arrived before the market processed it. It's less valuable if you're confirming something the line already reflects.
The practical habit is checking the line at the start of your monitoring session and again after completing the workflow. The comparison tells you whether your team news assessment is already priced in or whether there's residual value in acting on it.
A specific instruction worth adding to your closing prompt: "For each significant player status in these summaries, indicate whether you would expect the news to be line-moving if not yet priced. A confirmed absence of a key striker is line-moving. A doubt status for a fringe squad player is not. A goalkeeper doubt is market-specific - line-moving for clean sheet and BTTS markets but less so for match result."
That market-specificity instruction is what converts a team news summary into a market-specific action framework. The same player absence has different implications for different markets, and the prompt structure should reflect that rather than leaving you to make the translation manually under time pressure on a Friday afternoon when you've already got three other things open.
Anyway. The workflow is only as useful as the discipline with which you maintain it. Team news monitoring that happens when you remember and gets skipped when you're busy is selective information gathering with all the cherry-picking problems that entails. The value comes from running it consistently across every fixture you're active on, which means keeping the collection step fast enough that it doesn't become a reason not to bother. Ten minutes per fixture, once you've got the calibration document built and the prompt templates ready. That's the target.
FAQ
How do I handle team news that arrives too late for the full workflow - say, a confirmed team sheet dropped ninety minutes before kick-off?
A different, faster prompt for that specific situation. Paste the team sheet alongside your pre-existing assessment from the full workflow and ask: "The official team sheet has just been released. Compared to my pre-match team news assessment, what is confirmed, what is contradicted, and what remains unresolved? Specifically, identify any starting lineup surprises relative to expected availability and note which markets those surprises are most relevant to." That prompt takes two minutes and converts the team sheet from a raw data point into a structured comparison against your prior framework. The key is having the prior assessment already completed - without it, the team sheet comparison has nothing to compare against and you're back to processing it manually.
Some managers are consistently vague about injuries to the point of being useless. Does this workflow still produce anything worthwhile for their fixtures?
Yes, with adjusted expectations. For managers who are systematically uninformative in press conferences - and there are several at the top level who have turned injury obfuscation into a near-art form - the press conference source gets low weight in your calibration document and the workflow leans more heavily on beat journalists and training ground observation. The confidence levels will be lower across the board for those teams because the manager's information deliberately constrains what's publicly available. That's honest and useful output. A summary that tells you "high uncertainty on three players, confidence only on confirmed absences" for a specific manager is accurately describing the information environment for that club. Knowing the uncertainty is higher than usual is itself a meaningful input into whether to bet that fixture at all, or to wait until closer to kick-off when training ground reports have had time to filter through.
Is there a risk that using the same prompt structure across many bettors produces homogeneous assessments that cancel out any edge?
Only if everyone is using the same source calibration, which they won't be - because calibration is personal and built from individual tracking history. Two bettors using identical prompts with different calibration documents will produce different confidence weightings for the same raw information. The edge in this workflow doesn't come from the prompt structure, which is generic and freely shareable. It comes from the calibration document, which is yours. The same principle as the personal AI research assistant article - the model is the tool, the distinctive input is what you bring to it. A workflow anyone could copy still produces better individual output when personalised calibration is driving the weighting. Give the template to whoever wants it. The calibration is the part that compounds over time and can't be transferred.
