The Colour of Information: Understanding What Type of Edge You Actually Have

[Betting Forum]

Most betting analysis discussions skip a step that determines whether everything else in the discussion is worth having. Before asking whether a specific piece of analysis is correct, the prior question is: what type of information does this analysis represent, and has the market already incorporated it? The answer to that question determines whether correct analysis translates into edge or just into accurate agreement with the current line.

This is a conceptual article rather than a tactical one. It's about developing a framework for categorising betting information before deploying it - a taxonomy that helps bettors identify where in the information landscape their analysis actually sits, rather than assuming it sits in the most favourable category by default. Most people, in my experience of watching how bettors think about their own work, misidentify which category their analysis belongs to. The misidentification is almost always in the optimistic direction.

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The Four Colours​

Think of betting information as existing in four broad categories distinguished by how widely it's distributed and how completely the market has incorporated it. Colour is as good a metaphor as any - the categories shade into each other at the margins rather than having sharp boundaries, and the shade of a specific piece of information can change as the season progresses and as markets develop.

Red information is fully public and fully priced. The Premier League table. A team's goals scored and conceded. A manager's record since appointment. The headline injury news released through official channels. Every bettor in the market has access to this information, and the sophisticated models that price the market have processed it thoroughly. Using red information as the primary basis for a betting decision is essentially reconstructing the market's own assessment less accurately. You're competing against a system built specifically to incorporate this information faster and more precisely than any individual analyst can. The edge available from red information is, in competitive liquid markets, approximately zero.

Amber information is public but slowly or incompletely processed. This is where most genuine edge for individual bettors actually lives, and it's the category that requires the most nuanced understanding because the processing speed varies significantly by market, by competition, and by information type. Referee tendency data is amber - it's publicly available but the collection effort is high enough that most market pricing doesn't incorporate it fully. Artificial pitch effects, as covered in the earlier article in this series, are amber in lower leagues. xPoints divergences are amber in the early weeks of a season before the market has had time to incorporate the performance data behind the results. Weather variables - specifically the non-obvious ones like wind direction and pressing fatigue - are amber in the specific window between forecast confirmation and match time.

The key property of amber information is that it's technically accessible to anyone but practically incorporated by fewer participants than red information. The processing cost - time, data collection effort, analytical depth required to translate it into a price adjustment - creates a gap between the information being available and the market fully reflecting it. That gap is the edge opportunity.

Grey information is semi-private. It exists but is not uniformly distributed. A physio's knowledge of a player's specific injury status before it's officially disclosed. A journalist with a source inside the training ground who knows the lineup twenty-four hours before announcement. An agent aware of a transfer situation that will affect a player's motivation in this weekend's match. A data company that has scraped something from an obscure source that most competitors haven't found. Grey information is real, it's more valuable than amber precisely because fewer people have it, and it's mostly inaccessible to individual recreational bettors without specific structural advantages.

The important thing about grey information from a betting perspective is understanding that the market often processes it faster than it appears to from the outside. The line movement that seems to precede public information - the odds that shortened before the injury was officially announced, the total that moved before the weather forecast was widely discussed - is usually grey information being processed by the participants who have it. When you observe the market moving without an apparent public reason, you're seeing grey information being priced in.

Black information is genuinely private and inaccessible. What happens inside the training ground and the dressing room. The manager's actual read on a player's psychological state that hasn't manifested in any external signal. The specific tactical instruction for this game that won't appear in the pre-match press conference. The interpersonal dynamic between two players that's affecting their combination play. Black information isn't priced because it can't be, and it isn't accessible to anyone outside the circle of people with direct involvement. Some of this information eventually becomes grey as it leaks through training ground sources. Most of it remains permanently inaccessible.

Why Most Bettors Misidentify Their Information​

The consistent direction of misidentification is toward the amber and grey categories when the information is actually red. This is worth understanding carefully because it explains a large proportion of the gap between how bettors perceive the quality of their analysis and what their long-term P&L reflects.

Red information feels more insightful than it is because of how it's processed. A bettor who has watched twenty hours of this team's matches this season, who understands their tactical setup in genuine depth, who has formed a nuanced view of their strengths and vulnerabilities - that bettor feels like they have something the market doesn't. The experience of having done the work feels like an information advantage. But if the conclusions of that work are ones the market's aggregate assessment has already reached - if the tactical insight is visible in the public record and the model has incorporated it - then the depth of engagement has produced red information with amber effort costs attached. The analysis is correct and worthless as edge simultaneously.

The misidentification is made easier by a specific cognitive dynamic: bettors who are right about a team's quality feel that their correctness validates their information category assumption. If the team performs as expected and the bet wins, it confirms the implicit belief that the analysis was amber or grey. It was probably red. The win was the correct side of a correct probability assessment that the market shared - the bet was not positive expected value, it just happened to come in.

Social reinforcement compounds this. On betting forums and in group chats, analysis that matches the market is rarely challenged because it's also the consensus position. The feedback loop that would reveal whether analysis is genuinely amber - being right about things the market was wrong about - doesn't operate clearly in social contexts where group consensus and market consensus often align.

The Processing Speed Variable Within Amber​

Amber information isn't uniform, and within that category there's a spectrum of processing speed that determines how wide the gap between information availability and market incorporation actually is.

Fast amber processes in minutes to hours. The team news that a key midfielder is out - officially confirmed through the club's social media an hour before a Thursday afternoon fixture with low market liquidity. The market adjusts, but the adjustment can be incomplete in the window before full liquidity returns. This is amber information with a very narrow window. Capturing it requires either genuine speed advantages or systematic monitoring of the right sources at the right times.

Slow amber processes over days to weeks. The artificial pitch advantage for home clubs. The xPoints divergence that's been building for eight weeks. The referee tendency pattern that's emerged across the first four months of the season. These pieces of information don't move the line in a single dramatic adjustment - they're incorporated gradually as more participants build the analysis and more market participants act on it. The window is wider but the edge per fixture is smaller because the information is available to more participants who have had longer to find it.

Structural amber is the most durable. Information that requires specific data collection infrastructure, specific analytical depth, or specific knowledge of market mechanics to convert into edge. The goalkeeper distribution analysis from the earlier article. The squad depth calculation for European rotation. The detailed referee database. These require effort to build and maintain that creates a persistent processing gap regardless of how long the information has been technically available. The market can know something is theoretically priceable without ever fully pricing it because the cost of doing so isn't worth it for the available liquidity.

Understanding where within amber your specific analysis sits determines the urgency with which you need to act on it and the durability you can expect from the edge.

The Common Mistakes by Bettor Type​

Different types of bettors make characteristic errors in identifying their information category, and it's worth being specific about what those errors look like.

The football analyst type - someone with genuine tactical depth who watches extensively and builds a strong qualitative picture of teams - typically overestimates how amber their analysis is. The tactical insight they've developed is real. The problem is that tactical analysis of well-covered teams has been done by the market's aggregate participants to a depth that's hard to exceed. Their analysis is amber in feel and red in practice for the main markets they're most familiar with. The correction is to apply the genuine analytical depth in markets where it hasn't been done - lower leagues, specific prop types, competition contexts where the information is available but the analytical community hasn't focused.

The quant type - someone building models, working with xG data, building expected performance metrics - has the opposite blind spot. They assume that because their methodology is sophisticated and their data sourcing is systematic, the output is amber information. The more common reality is that their model is reconstructing a version of the market's own modelling using the same public data sources. If the inputs are all red and the methodology is standard in the analytical community, the output is red regardless of how much effort it took to produce. The correction is to identify data sources or analytical approaches that the standard modelling community hasn't incorporated, or to apply the methodology in markets thin enough that the standard modelling hasn't been done.

The sharp bettor type - someone primarily focused on CLV, line shopping, and market dynamics - is often more honest about their information category because their edge is explicitly structural rather than informational. They're exploiting the amber-to-red processing gap rather than claiming to have amber information about team quality. The risk for this type is assuming the structural edge scales in ways it doesn't - the account limitation problems described in the early articles in this series are precisely the mechanism by which structural edge arbitrage gets shut down.

The Semi-Private Trap​

Grey information deserves special attention because it's the category bettors most often believe they have access to when they usually don't.

The appearance of grey information is more common than the reality. A tweet from a beat journalist that seems to contain an inside source's view of a player's fitness. A forum post from someone claiming to have training ground access. A tip passed through a social network that traces back to someone who knows someone at the club. These feel like grey information because they come through social channels that feel less public than official announcements. They're often red information in grey clothing - widely distributed through the same social networks that feed every other bettor, arrived at the market simultaneously, and already processed into the current line.

Genuine grey information - the kind that actually moves lines when it hits the market - is almost always possessed by people who aren't sharing it publicly before they act on it. If it's in a forum post, it's been in the market for long enough that the line has already adjusted. If it's circulating in a group chat with twenty members, at least one of those members has already bet it. The social channels through which most bettors believe they receive grey information are more accurately described as slow-moving red information channels - the information was grey when it first emerged and had already decayed to red by the time it arrived in the forum.

This matters because the psychological experience of receiving what feels like a tip - something that came through a social channel, seems less public than official news, and feels like an advantage - produces confidence in a bet that isn't justified by the actual information category. The tip might be genuine. It might be accurate. It might still be red by the time it reaches you.

Where Each Type of Bettor Should Actually Be Looking​

The framework produces specific recommendations that differ by bettor profile, and being honest about which profile you fit is the prerequisite.

If your primary analytical input is qualitative football knowledge - tactical analysis, squad assessment, managerial tendency reading - the correct market context is thin markets, niche competitions, and specific prop types where the same analysis hasn't been done by the aggregate market participants. Championship prop markets. Lower-tier European competition result markets with limited data coverage. Specific player-level markets where the qualitative knowledge about that player's specific situation is more differentiated than the model can produce. Not the main Premier League match result market where the same analysis has been done more thoroughly by better-resourced participants.

If your primary analytical input is quantitative modelling - xG-based metrics, expected performance calculations, statistical analysis of historical patterns - the correct market context is either the niche markets where the modelling hasn't been done, or the timing windows within mainstream markets where specific amber information creates a processing gap. The xPoints divergence in weeks twelve to twenty-five. The referee data at Championship and lower-league level. The post-winter-break line adjustment before it fully corrects. Not the main market where the same modelling is done better.

If your primary edge is structural - line shopping, CLV capture, arbitrage across operators - the correct market context is wherever the best structural opportunities exist with the least account limitation risk. The exchange model described in the automation article. The operators who are slowest to adjust their lines after specific events. The market types where the structural margins are lowest. The specific timing windows where amateur money is most dominant and the market is most mispriced. The information category for this type is self-aware - you're not claiming amber information about team quality. You're claiming amber timing advantage in markets where the processing gap still exists.

The Practical Test​

The most useful single question for calibrating your information category isn't about the information itself. It's about your CLV.

If your bets consistently beat the closing line - if the price you got is regularly better than where the line settled after full market processing - you have evidence that you're acting on information or structural advantages that the market processed after you did. Your information was amber at the time of your bet and the market incorporated it subsequently. That's the definition of edge.

If your bets are broadly in line with the closing line - if you're getting roughly the same price that the market eventually settles at - you're not accessing amber information at a meaningful frequency. You're betting on red information at the time the market is confirming it as red. The information felt amber. The CLV says otherwise.

If your bets are regularly worse than the closing line - if you're getting worse prices than where the market settles - you're either consistently acting on red information after the market has already processed it, or your timing is systematically off. Either way, the CLV is telling you that the information you're treating as amber is being treated as red before you act on it.

This test is uncomfortable precisely because it's objective. The feeling of having good analysis doesn't survive a rigorous CLV check if the CLV check is negative. Most bettors who perform rigorous CLV tracking for the first time find that more of their analysis was red than they believed. That's not a conclusion to resist. It's the information you need to redirect effort toward genuinely amber territory.

FAQ​

Q1: Is there any genuinely amber information that most bettors have consistent access to without extraordinary effort?
Yes, and the articles earlier in this series are mostly an attempt to catalogue it. Referee tendency data, systematically collected and applied in the right competition context. xPoints divergences in the specific windows where the market hasn't yet incorporated them. Artificial pitch effects at lower-league level. International break disruption differentials in the specific fixtures where they're most pronounced. Weather variables beyond the obvious ones. These are all amber in the structural sense - available but requiring specific effort to convert into a price assessment more accurate than the current line. The effort required is real but not extraordinary. What keeps them amber is that the betting community hasn't collectively invested the preparation time to move them into the red category for the competitions where they matter most.

Q2: Is it possible to convert black information into a betting advantage without having direct access to it?
Indirectly, yes - through the grey information that black information generates as it leaks. A manager's private assessment of a player's psychological state is black, but the rotation decision that assessment produces is visible in the announced lineup and is grey briefly before becoming red when the lineup is confirmed. The training ground dynamic between two players is black, but it occasionally surfaces in the interaction patterns visible in match footage that an attentive watcher can observe and translate into amber information before the market incorporates it. The skill is in recognising the downstream signals of black information rather than accessing it directly - which is essentially what tactical analysis of playing patterns and training ground observable behaviour attempts to do.

Q3: Does the colour of information change across a career, and do experienced bettors naturally develop access to more amber information over time?
Yes to both questions. Information that was amber for a new bettor - the basic xG divergence analysis, the home-away split patterns for specific team types, the public referee data - becomes red as the bettor's model catches up to what the market has already incorporated. The experienced bettor who relies on the same amber information they found five years ago without continuously identifying new amber is progressively moving toward a red information diet as their original edges get priced in. Equally, over time, systematic data collection and network development can create genuine structural advantages that weren't available at the start - the referee database built over three seasons, the analytical framework developed through consistent application across hundreds of fixtures, the market timing intuitions built from sustained CLV tracking. Experienced bettors who continue finding edge are those who treat amber information identification as an ongoing process rather than a one-time discovery.