Overall Score

Each tipster is scored on a number of criteria – ROI, Profitable Months etc. The assessments are done based on their previous results and are graded on a scale of 1 (low) to 10 (high).

Each criteria has a “weighting” that indicates the importance of that criteria compared with all the other criteria.

The overall score is the weighted average of the individual score for each of he criteria multiplied by the weighting factor of that criteria.

Weighted average is a way of calculating a single average value that takes into account the importance or “weight” of each value being averaged. Here’s a simple explanation:

Imagine you have a bunch of numbers, and each number has a certain importance or “weight” attached to it. To find the weighted average, you multiply each number by its weight, add up all those products, and then divide by the sum of the weights.

Here’s the formula:

Weighted Average = Sum of (Value x Weight) / Sum of Weights

For example, let’s say you have three test scores: 80, 90, and 70, and they’re weighted equally. To find the weighted average, you’d add them up (240), and then divide by the total number of scores (3). So, the weighted average would be 80.

But if, for instance, the first score was worth twice as much as the other two, you’d calculate it like this:

Weighted Average = (  (80 x 2)+(90 x 1) + (70 x 1)  )/ (2 + 1 + 1)

That’s (160+90+70) / (2 + 1 + 1) which equals 82.5.

So, a weighted average gives more importance to some values over others based on their weights.

Back to Top

ROI – Return On Investment

Betting Return on Investment (ROI) is a measure used to evaluate the efficiency of a betting strategy or individual bets. It shows how much profit or loss you make relative to the amount you have wagered. Here’s how it works:

Calculating Betting ROI

To calculate the ROI in betting, you use the following formula:

ROI = ( Net Profit/Total Investment ) x 100%

Net Profit is the total amount of money you have won from your bets minus the total amount you have wagered.

Total Amount Wagered is the sum of all the money you have placed on bets.

Example Calculation

Suppose you placed several bets over a period and your betting activity looks like this:

  • Total amount wagered: $1,000
  • Total amount won: $1,200
  • Net profit: $1,200 (total won) – $1,000 (total wagered) = $200

Using the ROI formula:

ROI = ( 200/1000 ) x 100% = 20%

This means you have a 20% return on investment, indicating that for every dollar you wagered, you made an additional $0.20 in profit.

Importance of ROI in Betting

  1. Performance Evaluation: ROI helps bettors assess how effective their betting strategies are. A positive ROI indicates a profitable strategy, while a negative ROI suggests losses.
  2. Comparison Tool: ROI allows comparison between different bettors or betting systems, regardless of the amount of money wagered.
  3. Sustainable Betting: Understanding and optimizing ROI can lead to more sustainable betting practices, as it emphasizes profitability over time rather than short-term wins.

Factors Influencing Betting ROI

  1. Odds: Higher odds can lead to higher potential returns, but they often come with increased risk.
  2. Betting Strategy: Different strategies, such as value betting, arbitrage, or following tipsters, can impact ROI differently.
  3. Discipline: Consistency and disciplined bankroll management are crucial for maintaining a positive ROI.

Improving Betting ROI

  1. Research and Analysis: Study the events, teams, or participants thoroughly before placing bets.
  2. Value Betting: Look for bets where the odds offered by the bookmaker are higher than the actual probability of the event occurring.
  3. Avoiding Bias: Make decisions based on data and analysis rather than emotions or personal preferences.
  4. Bankroll Management: Manage your betting funds wisely to ensure you can withstand losing streaks and continue betting long-term.

In summary, Betting ROI is a key metric for any serious bettor as it provides a clear picture of the effectiveness and profitability of their betting activities. By understanding and monitoring ROI, bettors can make more informed decisions and improve their chances of long-term success.

Back to Top

PM – Profitable Months

Betting on sports or other forms of gambling can lead to both profitable and losing months due to several factors that influence the outcomes and overall profitability. Understanding these factors can help explain why some months are better than others:

Profitable Months

  1. Skill and Strategy:
    • Informed Decisions: During profitable months, bettors often make more informed decisions based on thorough research and analysis of the games or events they are betting on.
    • Effective Strategies: Use of effective betting strategies, such as value betting or matched betting, can lead to higher chances of winning.
  2. Luck:
    • Favorable Outcomes: Even with skill and strategy, luck plays a significant role. During profitable months, the outcomes of bets may align more often with the predictions.
  3. Market Conditions:
    • Favorable Odds: Sometimes the odds provided by bookmakers are more favorable or have higher value, leading to better returns on winning bets.
  4. Discipline:
    • Controlled Betting: Sticking to a disciplined approach, managing bankroll effectively, and avoiding impulsive bets can contribute to consistent profits.

Losing Months

  1. Unpredictable Events:
    • Unexpected Outcomes: Sports and other events can be unpredictable. Key players may get injured, or unexpected results can occur, leading to losses.
    • Bad Runs: Sometimes, even well-researched bets lose due to streaks of bad luck.
  2. Poor Decisions:
    • Insufficient Research: Making bets without adequate research or relying on gut feelings rather than data can lead to poor decisions.
    • Chasing Losses: After a few losses, bettors might try to chase their losses by making larger or more frequent bets, which often results in further losses.
  3. Market Misjudgment:
    • Misinterpretation of Odds: Misjudging the value of the odds or overestimating the probability of certain outcomes can lead to losses.
    • Bookmaker Advantage: Bookmakers generally have a house edge, and consistently overcoming this advantage requires skill and strategy.
  4. Psychological Factors:
    • Emotional Betting: Letting emotions influence betting decisions can lead to irrational bets. For example, betting on a favorite team regardless of the odds.
    • Lack of Discipline: Straying from a betting plan or staking more than one can afford to lose can quickly turn a month unprofitable.

Balancing Profitable and Losing Months

To increase the likelihood of profitable months and minimize losing ones, bettors can:

  • Maintain Discipline: Stick to a consistent staking plan and avoid impulsive betting.
  • Conduct Thorough Research: Analyze statistics, form, and other relevant factors before placing bets.
  • Manage Bankroll: Allocate a specific portion of money for betting and stick to it, ensuring that losses don’t deplete the bankroll.
  • Learn and Adapt: Continuously improve betting strategies based on past performance and stay updated with market trends and news.

By understanding and managing these factors, bettors can aim to increase their profitable months and mitigate the impact of losing ones.

Back to Top

Exp – Expectancy

Trading expectancy is a key concept in trading and investing that helps traders evaluate the potential profitability of their trading strategies. It quantifies the average amount a trader can expect to win or lose per trade over a series of trades. Understanding and calculating trading expectancy can guide traders in developing and refining strategies that have a higher probability of long-term success.

The concept of trading expectancy can be applied to bets since a bet is effectively a short-term trade.

Components of Trading Expectancy

Trading expectancy can be broken down into the following components:

  1. Win Rate (P_win): The percentage of trades that are profitable.
  2. Loss Rate (P_loss): The percentage of trades that are unprofitable. Since every trade either wins or loses, P_loss = 1 – P_win.
  3. Average Win (W_avg): The average amount gained on winning trades.
  4. Average Loss (L_avg): The average amount lost on losing trades.

Formula for Trading Expectancy

The formula for calculating trading expectancy (E) is:

Trade Expectancy ($) = Win (%) x Average Win ($) – Loss (%) x Average Loss ($).


  • Positive Expectancy (E > 0): Indicates that the trading strategy is expected to be profitable over the long term. Each trade, on average, adds to the trader’s wealth.
  • Negative Expectancy (E < 0): Indicates that the trading strategy is expected to lose money over the long term. Each trade, on average, depletes the trader’s wealth.
  • Zero Expectancy (E = 0): Indicates a breakeven strategy where no profit or loss is expected over time.

Example Calculation

Suppose a trader has the following statistics from their trading strategy:

  • Win Rate (P_win) = 40% (0.40)
  • Loss Rate (P_loss) = 60% (0.60)
  • Average Win (W_avg) = $200
  • Average Loss (L_avg) = $100

Plugging these values into the expectancy formula:

E = (0.40×200)−(0.60×100)

This means that, on average, the trader can expect to make $20 per trade over a series of trades.

Importance of Trading Expectancy

  1. Risk Management: By understanding the expectancy of their trades, traders can better manage their risk and make more informed decisions about position sizing and capital allocation.
  2. Strategy Evaluation: Trading expectancy helps in evaluating and comparing different trading strategies to identify the most profitable ones.
  3. Confidence in Trading: Knowing that a strategy has a positive expectancy can provide traders with the confidence to stick with their plan through losing streaks, which are inevitable in trading.

Improving Trading Expectancy

To improve trading expectancy, traders can:

  1. Increase the Win Rate: By improving trade selection and timing, traders can increase the proportion of winning trades.
  2. Enhance Average Win: Maximizing profits on winning trades through better exit strategies or letting profits run.
  3. Reduce Average Loss: Minimizing losses on losing trades through disciplined use of stop-loss orders and cutting losses quickly.
  4. Optimize Risk-Reward Ratio: Ensuring that the potential reward of a trade justifies the risk taken.

By continuously analysing and refining their strategies with the expectancy formula, traders can enhance their overall performance and profitability in the markets.

Back to Top

Skill – skill versus chance

To determine if a tipster’s results are due to skill rather than chance, you can use statistical methods to calculate the p-value. Here’s a step-by-step guide to performing this calculation:

Step 1: Formulate Hypotheses

  • Null Hypothesis (H0): The tipster’s results are due to chance.
  • Alternative Hypothesis (H1): The tipster’s results are due to skill.

Step 2: Collect Data

Gather the tipster’s predictions and the outcomes of the events they predicted. Typically, you need a series of binary outcomes (win/lose, success/failure).

Step 3: Choose a Statistical Test

The choice of test depends on the nature of the data and the number of predictions. Common tests include:

  • Binomial Test: If you have a series of binary outcomes (e.g., correct or incorrect tips).
  • t-Test: If you are comparing the tipster’s average returns to a benchmark.
  • Chi-Square Test: If you have categorical data.

Step 4: Perform the Binomial Test (example for binary outcomes)

If the predictions can be categorized as correct or incorrect (binary outcomes), you can use a binomial test.

  1. Calculate the Proportion of Successes:
                        p = Number of Correct Predictions Total Number of Predictions
  2. Set the Expected Proportion:
    Under the null hypothesis (pure chance), the expected proportion of correct predictions is 0.5 (assuming random guessing in a binary outcome scenario).
  3. Calculate the Binomial Test Statistic:
    The binomial test can be calculated using statistical software or tables. For simplicity, you can use Python or R for this calculation.

Step 5: Interpret the P-value

  • P-value: The probability of observing the results (or more extreme) assuming the null hypothesis is true.
    • A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, suggesting the tipster is skillful.
    • A large p-value (> 0.05) suggests insufficient evidence to reject the null hypothesis, indicating the results could be due to chance.

Example Interpretation

If the p-value is 0.03, there is a 3% probability that the tipster’s performance is due to chance, providing evidence that the tipster might be skillful.

Other Considerations

  • Sample Size: Ensure you have enough predictions to draw a meaningful conclusion.
  • Multiple Testing: If evaluating multiple tipsters or over multiple periods, adjust for multiple comparisons (e.g., using the Bonferroni correction).


To determine if a tipster’s success is due to skill rather than chance, you can use a binomial test for binary outcomes. Calculate the p-value and interpret it within the context of your chosen significance level. A low p-value suggests skill, while a high p-value indicates results may be due to chance.

Back to Top

ELLR – Estimated Longest Losing Run

To calculate the estimated longest losing run based on win rate and the number of bets, you can use a statistical concept called the geometric distribution.

The geometric distribution models the number of failures before the first success (win in this case), given a probability of success (win rate).

To find the estimated longest losing run, you can use the formula:

    Estimated Longest Losing Run = log10(n) log10(1-p)


  • n = Number of bets
  • p = Probability of success (win rate)

This formula gives you an approximation of the longest losing streak you can expect based on the provided win rate and number of bets.

Back to Top

Risk – Risk versus Reward

Calculating risk based on average and standard deviation is a common method used in finance and statistics to quantify the uncertainty or volatility associated with an investment or a set of data points. Here’s how it’s done:

  1. Average (Mean):
    • The average, or mean, is calculated by summing up all the data points and dividing by the total number of data points.
    • It represents the central tendency of the data set.
  2. Standard Deviation:
    • The standard deviation measures the dispersion or spread of the data points around the mean.
    • It indicates how much the individual data points deviate from the mean.
    • A higher standard deviation implies greater variability or risk.
  3. Calculation of Risk:
    • In finance, risk is often measured as the standard deviation of the returns of an investment.
    • For example, if you have a set of investment returns over a period of time, you would calculate the standard deviation of those returns.
    • The higher the standard deviation, the riskier the investment is considered to be, because it indicates greater variability in returns.
  4. Interpretation:
    • When looking at the average and standard deviation together, you can assess both the expected return and the risk associated with an investment or a data set.
    • Higher average returns are desirable, but they need to be weighed against the level of risk, as indicated by the standard deviation.
    • An investment with a high average return but also a high standard deviation may have higher potential returns, but it also comes with higher risk.
    • Conversely, an investment with a lower average return and a low standard deviation may be considered less risky but may offer lower potential returns.

The Sharpe Ratio is a measure used to evaluate the risk-adjusted return of an investment or a portfolio. It was developed by Nobel laureate William F. Sharpe and is widely used by investors to assess the return earned in excess of the risk-free rate per unit of volatility or risk taken. The formula for the Sharpe Ratio is:

Sharpe Ratio=(Rp−Rf) / σp


  • Rp is the expected return of the investment or portfolio.
  • Rf is the risk-free rate of return, typically represented by the return on a short-term government bond or similar low-risk investment.

The Sharpe Ratio essentially quantifies the excess return per unit of risk taken. A higher Sharpe Ratio indicates a better risk-adjusted return, meaning that the investment or portfolio has generated more return for each unit of risk assumed. Conversely, a lower Sharpe Ratio suggests that the investment or portfolio may not be adequately compensating investors for the level of risk involved.

It’s important to note that the Sharpe Ratio is most useful when comparing investments or portfolios with similar risk profiles. Additionally, it assumes that returns are normally distributed, which may not always be the case in reality. Despite its limitations, the Sharpe Ratio remains a valuable tool for investors in assessing risk-adjusted performance.

In summary, while the average provides insight into expected returns, the standard deviation offers a measure of the variability or risk associated with those returns. Both metrics are important for making informed decisions in finance and statistics.

Back to Top

Value – Value for Money

Calculating this Value-for-Money (VfM) figure allows comparison of tipsters based on their subscription costs.

The Value figure is simply the cost of 1% of the ROI being produced by the tipster.

It is calculated as follows:

VfM = Monthly Subscription Cost Average ROI percentage value

Example Calculation

Suppose a tipster has a monthly subscription fee of £39 and has produced an average ROI of 20% then the subscription cost per 1% of ROI is:

VfM (tipster 1) = £3920  = £1.95 per 1% of ROI

When comparing this with a different tipster whose monthly subscription is £59 and has produced and average ROI of 25%.

VfM (tipster 2) = £5925  = £2.36 per 1% of ROI

Tipster 1 offers better value because they are charging less for 1% of ROI.

Back to Top

Stakes – big bets or small bets?

Knowing the ROI and the number of tips per day and the advised stake points you can work out how much you would need to bet to cover the tipster’s monthly subscription cost. Betting larger stakes than that per point could then give you a clear profit.

The difficulty is however that you don’t know what the future ROI is going to be – it could even be negative – a losing month. You also don’t know how many tips the tipster will advise during the next month – although you might estimate it as the average of say the last 3 completed months.

But doing the calculation can provide an indication of the scale of betting levels needed to cover the subscription costs and perhaps make a profit. Will it be something like £10 a day or more like £100 a day. This will be linked to the size of betting bank you will need to follow the tips.

The calculation for the stake level required is:

Stake = (Month Anticipated Profit + Subscription Cost per Month) (Tips per Month x Average Stake x ROI)

This calculation can only give you a scale of things. It will  never work out this way in real life – the ROI as the month progresses will vary, the number of tips per day will vary and the advised points per tip can change.

Back to Top

Bank – Betting Bank size and management

Managing a betting bank effectively is crucial for anyone involved in sports betting. It will also be based on personal preferences and tolerance for risk.

The method I prefer is betting a percentage of available bank on a regular basis.

Here are some general tips to help you manage your betting bank:

  1. Set Aside a Betting Bank: Determine an amount of money that you are comfortable with using as your “betting bank.” This should be money that you can afford to lose without impacting your financial stability.
  2. Use Proper Stakes: A common rule of thumb is to avoid staking more than 1-2% of your total betting bank on any single bet. This helps to minimize the risk of large losses and allows your bank to withstand losing streaks.
  3. Maintain Discipline: Stick to your staking plan and avoid chasing losses by increasing your stakes to recoup previous losses. Emotional betting often leads to poor decision-making and can quickly deplete your betting bank.
  4. Analyse Your Bets: Conduct thorough research and analysis before placing any bets. Consider factors such as team form, player injuries, weather conditions, and historical performance. Make informed decisions based on data rather than gut feelings.
  5. Diversify Your Bets: Avoid placing all of your bets on a single outcome or event. Diversifying your bets across different sports, markets, and types of bets can help spread risk and increase your chances of success.
  6. Set Realistic Goals: Be realistic about your expectations and set achievable goals for your betting bank. It’s unlikely that you’ll double your bank overnight, so aim for steady, sustainable growth over time.
  7. Track Your Results: Keep detailed records of all your bets, including the stake, odds, and outcome. Analyzing your betting history can help you identify strengths and weaknesses in your strategy and make adjustments accordingly.
  8. Review and Adapt: Regularly review your betting performance and bankroll management strategy. Learn from your mistakes and successes, and be willing to adapt your approach as needed.
  9. Consider Value Betting: Focus on identifying value bets, where the odds offered by the bookmaker are higher than the true probability of the outcome occurring. Value betting can lead to long-term profits if done consistently.
  10. Stay Informed: Stay up-to-date with the latest news, developments, and trends in the sports you’re betting on. This can help you make more informed decisions and stay ahead of the competition.

By following these tips and maintaining a disciplined approach to bankroll management, you can increase your chances of success and protect your betting bank from unnecessary risks.

Back to Top