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The classification problem is harder than it sounds. A line moving from 1.90 to 1.80 on the home team tells you something moved it. What moved it is not visible in the price change itself. It could be sharp money agreeing with your analysis, which is reassuring. It could be sharp money identifying something your analysis missed, which is information. It could be public money flowing on the popular side, which is neutral to slightly negative for your pre-existing position. It could be injury news you haven't seen yet, which is urgent. Same price movement, four completely different implications.
Most bettors process line movements in real time, intuitively, without a record. Their classification is whatever explanation feels most plausible given what they currently know, which means it's heavily influenced by recency, by whatever news they happened to see that day, and by whether they already have a position. That's not classification. That's rationalisation dressed up as analysis.
A personal signal log changes the epistemics of this. Not dramatically, not immediately - but over time, a logged and LLM-assisted database of movements, probable causes, and outcomes tells you things about specific competitions and markets that intuition never accumulates into.
What the Log Needs to Capture
Before the prompts, the structure. Same lesson as the referee database and the manager database - design the fields before you start collecting.
The minimum useful log entry has the following fields. Fixture, including competition and date. Market being monitored - match result, Asian Handicap line and value, total goals line, specific props if relevant. Opening line and time observed. Movement direction and magnitude. Time of movement - approximate is fine, but Thursday morning versus Friday evening versus Saturday morning is a meaningful distinction. Whether any news event preceded the movement - injury report, press conference quote, training ground report. Any cross-operator comparison available - did the movement appear at one book only or across multiple simultaneously. Your initial classification of the probable cause. And a final field populated after the match: what actually happened with the team news and whether the pre-match picture retrospectively clarified what drove the movement.
That last field is the one that makes the database compound over time. Without outcome data, you're logging observations without ever closing the loop. With it, you're building a calibration record - which types of movement, in which competitions, at which times, tend to retrospectively classify as sharp versus public versus news-driven. That record is what eventually produces signal thresholds that are yours rather than borrowed from generic betting theory.
The log doesn't need to be complex. A spreadsheet with those fields, one row per movement observation, is sufficient. The LLM analysis sits on top of the raw log. You're not asking the model to replace your judgement about a specific movement in real time - you're asking it to help you find patterns in the accumulated record.
The Three Movement Types and Why Classification Is Uncertain
Worth establishing what you're actually classifying before designing prompts to classify it, because the categories are less clean than the framework implies.
Sharp money movement has a specific signature when it's unambiguous. A line moves significantly at a sharp-facing book - Pinnacle is the benchmark - before moving at recreational books. The movement is against public sentiment. It happens in the Thursday to Friday window before public money has arrived in volume. No news event preceded it. That combination points toward informed money and the classification confidence is reasonably high.
The problem is that most movements you'll observe aren't unambiguous. A line moves Friday afternoon at a recreational book. A popular team's odds shorten. It might be sharp money. It might be the public backing the team they like. Without a Pinnacle comparison and without knowing the volume behind the movement, you cannot distinguish these cleanly. Anyone who tells you they can is overconfident.
Public money movement is cleanest to identify in hindsight rather than in real time. Heavy public teams - historically popular clubs, recent media darlings, teams on winning runs - shorten for reasons that have nothing to do with analytical information. If you track the movement pattern of fixtures involving these teams across a season, the signature becomes visible: consistent shortening regardless of analytical merit, often reversing toward closing line as sharp money takes the other side. In real time, a single movement is hard to classify. Across fifty movements involving the same club, the pattern is clearer.
Injury news movement has the cleanest signature of the three. A discrete jump - not a gradual drift but a sudden shift - at a specific time. Often appearing first at sharp-facing books and propagating to recreational books within minutes. If it coincides with a press conference window or a journalist's Twitter post, the cause is usually identifiable. The interesting cases are the injury movements that precede any public news by an hour or more - those are the movements where the question of how someone knew the information before it was public is worth asking.
The classification uncertainty in all three categories is real and should be explicit in your log. The useful field isn't "cause: sharp money" as a confident assertion. It's "probable cause: sharp money, confidence: medium, basis: Pinnacle movement preceded recreational book movement by two hours, no preceding news event, movement against public sentiment for this competition." That's a classification you can learn from. The confident assertion is a classification you're fooling yourself with.
The Real-Time Classification Prompt
When you observe a movement worth logging, you run this prompt before your classification confidence degrades. The more time passes between observing a movement and logging it, the more your recollection is contaminated by subsequent information.
"I'm logging a line movement in my signal database. Here is what I observed: [fixture, competition, market, opening line, movement, time of movement, time relative to kick-off, any preceding news events, cross-operator information if available]. Here is my source reliability calibration for this competition's information environment: [paste relevant section from calibration document]. I want you to produce a classification entry with the following structure. First, list the possible causes for this movement in order of prior probability given the information I've described - sharp money, public money, news-driven, or mixed. For each possible cause, describe what observable features would be consistent with it and what features would be inconsistent with it. Second, give me your assessment of the most probable cause and your confidence level - high, medium, or low. Third, tell me what additional information, if observed before kick-off, would most change the classification. Do not give me a single confident answer. Give me the probability-weighted assessment with explicit uncertainty."
That last instruction is doing the most important work. Without it, the model will give you a clean answer - "this looks like sharp money movement" - because clean answers are what LLMs are trained to produce and what people asking questions typically want to receive. The explicit uncertainty instruction forces a different output: a structured assessment that acknowledges what the data doesn't tell you, which is the honest version of the analysis.
The "what would change the classification" field converts the log entry from a static observation into an active monitoring trigger. If you log that a Pinnacle movement preceding recreational books with no news precedent probably indicates sharp money, but note that a press conference injury mention in the next three hours would reclassify it as news-driven, you have a specific thing to monitor before the line settles.
Building the Pattern Database
Individual log entries are observations. The database is patterns. The transition from one to the other requires enough entries per category to produce signal rather than noise - which means the database takes months to build before it produces the most useful output.
The pattern analysis prompt runs on accumulated entries rather than individual movements. Once you have at least thirty entries in a specific competition or market category, you run something like this:
"The following are logged line movement entries from [competition] over [time period]. Each entry includes the movement details, my classification of probable cause, my confidence level, and the retrospective outcome field where I've noted what the team news and match actually revealed. I want you to identify patterns across these entries. Specifically: do movements of a certain type in this competition tend to be correctly classified at high confidence, or does my high confidence classification frequently turn out to be wrong? Are there movement signatures in this competition that I'm classifying as uncertain but that retrospectively resolve cleanly in a specific direction? Is there a time-of-week pattern in which types of movements this competition tends to produce? Identify genuine patterns only - flag any finding based on fewer than fifteen entries as low confidence and do not include it in the main findings."
The retrospective outcome field is what makes this prompt useful. Without it, you're pattern-matching on your own classifications, which just tells you whether you're internally consistent. With it, you're pattern-matching on whether your classifications turned out to be right - which is the calibration question that matters.
The fifteen-entry threshold instruction is the same discipline as the betting history leakage analysis prompts. The model will find patterns in five entries if you let it. Those patterns are noise. Fifteen entries per category is a rough minimum for treating a finding as directional rather than coincidental.
Competition-Specific Calibration
The signal log's most valuable output over time is competition-specific calibration that adjusts your interpretation framework based on how that competition's line movements actually behave.
Some competitions have distinctive movement signatures. Markets where sharp money is more consistently active produce movements with cleaner sharp money signatures. Markets where information processing is slower produce more frequent news-driven jumps that weren't anticipated by the opening line. Markets where public sentiment is strong - major derbies, rivalry fixtures, matches involving historically popular clubs - produce consistent public money patterns that look like sharp money if you're not calibrated for that competition.
The calibration prompt runs seasonally rather than weekly:
"The following is my complete line movement log for [competition] over the past season. I want you to produce a competition-specific calibration summary covering: the most common movement type logged for this competition and whether retrospective outcomes confirm the classification accuracy; the time-of-week pattern for meaningful movements in this competition; whether injury news movements in this competition tend to be preceded by journalist social media posts or appear without preceding public information; and whether there are specific market types within this competition where my classification accuracy is meaningfully higher or lower than the overall average. This calibration summary will be used as context in future classification prompts for this competition - frame it accordingly."
That last instruction - frame it for use as future context - produces an output structured for insertion into future prompts rather than structured as a standalone report. The calibration summary then sits in your competition-specific context document alongside the source reliability calibration, and both get pasted into the classification prompt for that competition going forward.
The compounding here is real. A classification prompt informed by eighteen months of your own movement log data for a specific competition is a genuinely different analytical tool than the same prompt run cold. The model isn't smarter - your context is richer.
The Signal Threshold Question
At some point the database is large enough to ask the threshold question directly: what movement magnitude, in what time window, in this competition and market, has historically been worth acting on before it settles?
This is the prompt most people want to run first, and the reason to resist running it until the database is genuinely large enough. A threshold derived from thirty entries is a pattern that may or may not persist. A threshold derived from two hundred entries in a specific competition, with retrospective outcome validation, is a calibrated signal. The difference matters because acting on an uncalibrated threshold costs you money in the same way acting on an overfitted backtest does.
When the database is large enough - call it one hundred and fifty entries in a specific competition and market type, with at least fifty that have been retrospectively classified - you run this:
"The following is my line movement log for [competition, market type] with retrospective classifications. I want to identify movement patterns that have historically been worth acting on before the line settled further. Specifically, identify any combination of movement magnitude, time window, and classification type where the retrospective outcome consistently indicated the movement was informative rather than noise. Express any finding as a threshold with a confidence interval, not a clean rule - I want to know how reliable the pattern is, not just that it exists. Flag any threshold finding based on fewer than twenty supporting entries as low confidence. Do not recommend any betting action - just describe the historical pattern."
The "express as a threshold with a confidence interval" instruction stops the model producing a rule that sounds more reliable than it is. A finding that says "movements of X points or more at Pinnacle between Thursday and Friday morning in this competition have retrospectively classified as sharp money in seventeen of twenty-two cases" is useful. A finding that says "movements over X points at Pinnacle indicate sharp money" is overconfident. The difference matters when you're deciding whether to act on it.
What the Log Will and Won't Tell You
Being direct about this before the FAQ.
The log will tell you whether your intuitive classifications are accurate over time. Most bettors will find they're overconfident about movement classification - high confidence calls that turn out wrong more often than high confidence implies. That finding alone, uncomfortable as it is, is worth the effort of building the database.
The log will tell you whether specific competitions have distinctive movement patterns you can exploit. Some will. Most won't be dramatically different from your prior assumptions, but the margins matter and competition-specific calibration adds genuine value over generic theory.
What the log won't tell you is what to bet. It tells you what information a movement contains. Converting that information into a betting decision still requires your analysis of the fixture. A correctly classified sharp money movement tells you informed money disagrees with the current price. It doesn't tell you whether the informed money is right. Sharp money is right more often than public money and more often than recreational bettors. It is not right always. The log is an input to your decision, not a replacement for it.
Anyway. The database doesn't produce its most useful output for six months minimum. That's a genuine barrier and worth acknowledging rather than glossing over. If you want something that helps immediately, the real-time classification prompt adds value from the first entry. If you want competition-specific calibration that changes how you interpret movements in a specific market, you're building toward something that takes a season to develop. Both are worth doing. Just know which one you're doing.
FAQ
Can I seed the database with historical movements I remember rather than starting from scratch?
Yes, with a significant caveat. Retrospectively logged movements are contaminated by outcome knowledge - you remember the movements that turned out to be informative more clearly than the ones that turned out to be noise, which is exactly the survivorship bias the prospective log is designed to avoid. If you retrospectively seed the database, flag those entries explicitly as retrospective and weight them lower in any pattern analysis prompt. Use them to build the initial structure and the habit of logging, but treat the prospective entries - logged at the time of observation, before outcome - as the reliable data and the retrospective entries as directional context at best. The model won't automatically distinguish between them. You have to build that distinction into the prompt instructions.
How do I handle movements that happen while I'm not monitoring - overnight, or during work hours?
Two-part answer. First, the movements you miss are a real limitation and no workflow eliminates them entirely. The log captures what you observe, not the full movement history of every line you care about. Second, the gap is smaller than it feels. For competitions where you have calibrated patterns from the database, you can retrospectively classify a movement you missed by checking the time stamps on cross-operator comparisons using historical odds from Oddsportal or similar. The classification confidence will be lower for retrospectively observed movements, but the pattern database still benefits from them if you flag the retrospective observation explicitly. Over time, the most important movements in your target competitions tend to happen in windows you can monitor if you've built the Thursday to Saturday rhythm described in the line opening article.
The model keeps classifying ambiguous movements as "likely sharp money" even when I instruct it to express uncertainty. How do I fix this?
Add two specific instructions that override the tendency toward clean answers. First: "For this classification, I want you to explicitly state the base rate of sharp money movements versus public money movements in recreational-facing markets before drawing any conclusion about this specific movement. If the base rate is uncertain, say so." The base rate instruction forces the model to acknowledge prior probabilities before updating on specific evidence, which produces more calibrated outputs. Second: "If your confidence in the classification is low, I want the low confidence assessment rather than a higher confidence assessment that sounds more useful. An honest low confidence classification is more valuable to me than a confident wrong one." That second instruction directly addresses the tendency to produce decisive-sounding output. Some models need it stated more bluntly than others. If it still produces overconfident classifications after both instructions, add a worked example of the output format you want - showing a correctly uncertain classification explicitly - and ask it to match that format.
