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That's what this is.
The workflow takes roughly ninety minutes per week once it's set up. The setup itself - building the baseline the whole thing depends on - takes a few hours across the first two or three weeks. After that it's a repeatable process: pull transcripts, run the prompts, check the output against your baseline, decide whether there's a signal worth acting on before the lines move.
Step by step. You can start Monday morning.
What You're Actually Looking For
Before the workflow makes sense, the target needs to be clear. You're not trying to predict match outcomes from press conference transcripts directly. That's not what this produces and anyone who tells you otherwise is overselling it.
What you're looking for is one of three things. First, genuine team news that hasn't been priced yet - a manager's language around a specific player that signals availability or non-availability before the official injury list appears. Second, tactical or motivational signals that suggest the coming match will be approached differently from recent fixtures. Third, linguistic drift - the slow change in how a manager talks about his squad, his situation, or specific players over weeks - that correlates with performance changes before results confirm them.
The third one is the most valuable and the hardest to see without a systematic approach. That's why the baseline matters. You can't detect drift without a reference point.
Step One: Build the Baseline (Weeks One Through Three)
The baseline is a record of how a specific manager communicates when things are normal. Not when things are good, not when things are bad - when things are routine. It's the linguistic fingerprint you compare everything else against.
Pick two or three managers to start. More than that and the setup phase becomes unwieldy. Choose managers whose press conferences are reliably transcribed - Premier League managers are the easiest because outlets like BBC Sport, The Guardian, and the clubs' own websites publish full or near-full transcripts within a few hours of the session. Championship managers are patchier but manageable. Below that, you're often working from journalist summaries rather than full transcripts, which reduces the signal quality significantly.
For each manager, find five to eight transcripts from periods that look stable - mid-table in form, no transfer window noise, no injury crisis, no boardroom speculation. These are your baseline documents. You're going to use them to establish the reference point the whole analysis depends on.
Run this prompt across each set of baseline transcripts:
"I'm going to give you a series of press conference transcripts from the same football manager. Read them carefully, then produce a baseline communication profile covering: typical answer length and detail level, characteristic phrases or sentence structures this manager uses repeatedly, how directly he addresses injury and selection questions, his typical language around upcoming opponents, any hedging patterns or qualifiers he uses habitually, and his default emotional register. This profile will be used to detect future deviation from normal communication patterns. Quote specific examples from the transcripts to support each observation."
The quote instruction matters here for the same reason it mattered in the referee database workflow. Paraphrased observations lose the specific language markers that make future comparison possible. You want the actual phrases, not the model's characterisation of them.
Save the output profile somewhere you can reference quickly. A simple document per manager works. Label it clearly with the manager's name, the transcripts used, and the date range they cover. You'll be comparing new transcripts against this profile every week going forward.
Step Two: The Weekly Transcript Pull (Monday and Thursday)
Premier League managers typically hold press conferences on Friday before weekend fixtures, with some Thursday sessions for clubs in European competition. The Championship schedule is similar. The transcripts appear within two to four hours of the session on club websites and major sports outlets.
For your Monday workflow - and Monday is when you're reviewing the previous week's signal, not pulling new data - you're working with the transcripts from the previous Thursday and Friday that you saved at the time.
The pulling process is straightforward. BBC Sport's match preview pages link to or quote manager press conferences. The club's official website usually has the fullest version. The Guardian's football section publishes extended quotes for top-flight managers. For some clubs, local newspapers carry fuller transcripts than nationals because they have dedicated beat reporters at every session.
Save the transcript as plain text. Remove interviewer questions if you can identify them - you want the manager's speech, not the journalist's framing. If the transcript is fragmentary - quotes rather than full session text - note that before you run the analysis, because fragmented transcripts produce less reliable output.
Step Three: The Weekly Analysis Prompt
Once you have the week's transcript, run this against the baseline profile you built:
"I'm going to give you a press conference transcript from a football manager, followed by his baseline communication profile. Compare the transcript against the baseline and identify: any deviation from his typical answer length or detail level, any phrases or hedging language that doesn't appear in his baseline patterns, how his language around injuries and selection differs from his baseline approach, any change in emotional register compared to the baseline, and any specific players or opponents mentioned in ways that seem unusual relative to his normal communication style. For each deviation you identify, quote the specific passage from the transcript and explain what makes it unusual relative to the baseline. If you find no meaningful deviations, say so clearly."
That last instruction is the one most people drop and most need to keep. A prompt that only asks for deviations without asking the model to report a clean bill of health when warranted produces a model that finds something to flag every time. False positives are useless - worse than useless, because they erode your confidence in the signals that are real.
The output will vary. Some weeks there's nothing worth noting - the manager sounds exactly like he always does, the injury question got the standard non-answer, the opponent section was boilerplate. That's useful information. It means you don't spend time looking for a signal that isn't there.
Other weeks there'll be something. The manager who normally gives detailed tactical previews suddenly speaking in generalities. A player who's been mentioned positively for three weeks suddenly absent from the answers. The pronoun shift from "we" to "they" when discussing the squad's preparation - a pattern the NLP article covered at length, and one that's detectable even in manual comparison once you know what you're looking for.
Step Four: The Injury Language Filter
Injury signals deserve their own prompt because they're the most time-sensitive and the most directly line-moving category of information.
Run this separately from the general analysis:
"Read this press conference transcript and extract every mention of player availability, fitness, and injury status. For each player mentioned: quote the exact language used, categorise the certainty level as definite available, likely available, uncertain, likely unavailable, or definite unavailable based on the language, and flag any language that seems deliberately evasive or notably more or less specific than you'd expect for a straightforward fitness update. If a player who has been in recent squads is not mentioned at all, note that absence."
The "note that absence" instruction catches something the general analysis prompt often misses. Managers sometimes communicate player unavailability by simply not mentioning them rather than by saying anything specific. If a player who's started three consecutive matches doesn't appear in a pre-match transcript at all - no mention of fitness, no mention of the upcoming fixture in relation to them - that silence is itself data.
This prompt runs fast. Most transcripts have five or six player-relevant passages and the extraction takes the model about thirty seconds. Cross-reference the output against the official injury list when it appears - typically Friday afternoon for Saturday fixtures. Where your language analysis flagged uncertainty and the official list confirms it, you're getting signal early. Where they diverge, that divergence is itself worth noting for calibration.
Step Five: The Drift Check (Monthly)
The weekly prompts catch acute signals - sudden changes in language around a specific player or fixture. The drift check catches the gradual changes that accumulate over weeks and don't show up in any single transcript but are visible when you compare across time.
Once a month, take the last four weeks of analysis outputs - not the transcripts, the outputs from your weekly prompt - and run this:
"I'm going to give you four weeks of analysis outputs comparing a football manager's press conferences against his baseline communication profile. Across these four outputs, identify any patterns that appear repeatedly - deviations that have occurred in multiple weeks and may indicate a genuine shift in communication behaviour rather than a one-off variation. Distinguish between patterns that are trending (appearing more strongly in recent weeks), stable (consistent across all four weeks), and fading (present early but absent recently). For each pattern you identify, quote the supporting evidence from the weekly outputs."
This is the linguistic drift detection the NLP article described as the most valuable channel. A manager who's been slightly evasive about a specific player for four consecutive weeks is telling you something different from a manager who gave one unusual answer then returned to normal. The monthly prompt catches the former. The weekly prompt usually catches only the latter.
The drift check adds maybe twenty minutes to your monthly workload. The outputs are worth reviewing carefully - these are the signals most likely to move lines when they eventually surface in injury list confirmations or selection decisions, because the market hasn't been watching the transcript sequence the way you have.
Connecting the Signal to the Bet
A workflow that produces signals but doesn't connect them to decisions isn't useful. The connection is straightforward but worth stating explicitly.
Injury language signals connect most directly to player prop markets, team clean sheet props, and total goals lines where the absent or uncertain player materially affects the expected output. The set piece specialist absence article is the clearest example - if your injury language filter is flagging uncertainty around a team's primary delivery specialist three days before a fixture, that's a total goals input that isn't priced yet.
Motivational and tactical signals connect to match result lines and Asian Handicap markets, but with more caution. The signal-to-noise ratio is lower here because managerial language is sometimes deliberately misleading about tactical intent. A manager who wants his opponent uncertain about his approach has an incentive to sound unpredictable. Weight these signals accordingly.
Linguistic drift signals - the monthly check - connect most usefully to outright markets and to the next four to six match result lines for the affected team. A manager showing consistent drift toward evasiveness and reduced positivity over a month is sending a signal about underlying squad dynamics that typically precedes a run of underperformance. Whether the market has priced that depends on recent results. Often it hasn't, because the market prices results, not transcript sequences.
The Thursday line opening article covered the information timeline. This workflow sits at the early end of that timeline - the transcript analysis runs before the official team news, before the training ground reports from Friday, before Saturday morning price movements. That sequencing is the whole point. You're not processing information faster than the market. You're processing a different input earlier.
Anyway. The workflow is genuinely implementable. It requires a few hours of setup, ninety minutes a week, and the discipline to report a clean analysis when the transcript is unremarkable rather than manufacturing a signal that isn't there. That last part is the hardest bit. The temptation to find something interesting in a Tuesday press conference where nothing is interesting is real, and it's exactly the cognitive distortion the CBT article described as confirmation bias in a research context.
The baseline is what makes the whole thing honest. Without it you're just reading transcripts and calling it analysis.
FAQ
Q: Can I use this workflow for managers who rarely do full press conference transcripts?
Partially. Journalist summary quotes are less reliable than full transcripts because you're working with a selection someone else made - and that selection is often biased toward quotable moments rather than representative language. For managers where only summaries are available, narrow the workflow to the injury language filter specifically, since that information is usually present even in fragmentary quotes. Drop the baseline drift analysis because you can't build a reliable baseline from selective quotation. The signal is weaker but not worthless.
Q: How do I handle managers who are consistently evasive as a baseline pattern?
Some managers - Jose Mourinho at his peak, Pep Guardiola on opponent-related questions, several Championship managers who've been burned by transfer speculation - communicate evasively as their normal mode. Your baseline captures this, which means genuine deviation shows up as unexpected specificity or unexpected openness rather than as evasiveness. The analysis works in the same direction, just inverted. A manager whose baseline is evasive suddenly giving detailed availability updates is as notable as a normally forthcoming manager going quiet. Build the baseline honestly from their actual communication patterns and the deviation detection handles itself.
Q: How quickly do line movements typically follow the signals this workflow catches?
It varies considerably by signal type and by how visible the signal is in the transcript. Explicit injury language - a manager saying a player "has a knock we're monitoring" - moves lines within an hour of the transcript appearing because every sharp bettor is monitoring these sessions. Subtle linguistic drift or the absence of a player from the conversation is slower, sometimes not priced until the official team sheet. That gap between subtle signal and market incorporation is where the workflow produces its clearest edge. The explicit signals are useful for timing if you're already positioned from other analysis, but the subtle ones are where the real information asymmetry sits.
