Filtering the Data

You can filter the data set used to generate the visual to your own preference. Changing any option will automatically reload the bell curve. The settings will also be saved to your account automatically. You can try this yourself with the bell curve above (filters will not be saved to your account in this guide).

Min races

This filter sets the minimum number of races a greyhound must have participated in to be included in the visual. Any greyhound with fewer than the minimum will not generate a visual.

Max races

This filter sets the maximum number of most recent races to be included from a greyhound's data set to generate the visual. If Max races is set to 6, but the greyhound has raced 20 times previously, only the most recent 6 race times will be used.

Recency

This filter limits the data to races that occurred within a specific timeframe. Only races that have taken place within the specified timeframe will be considered. This allows you to analyse recent performance and exclude older data that might not be as relevant.

Filter by distance and...

The data filters by the same distance and then allows you to narrow down the data based on your preference. Each dog's data set will be filtered to match the current race's characteristics. Available options are: Track, Trap and Grade. For example, selecting Trap and Grade will only show curves where, in previous races, each dog was in the same trap as this race and had the same grade.

Note: HP grades are not included in any bell curve.

The Legend

On the visual's axis, is a legend that colour coordinates the visual with each different trap number. The number in the brackets is how many races from the dog's data set are being used to generate the visual. Clicking on a dog's legend, will hide the dog from the visual. Hovering the mouse cursor over a dog's legend, will highlight the dog on the visual.

The Tooltip

Moving the mouse cursor over the bell curves will reveal a tooltip box showing what percentage of the bell curve is faster (further left) than the highlighted time. Try it yourself with the bell curve above.

Bell Curve Choices

These three buttons allow you to change which bell curve visual to display.

Calculated displays bell curves generated using each dog's historic Calculated Finish times.

Sectional displays bell curves generated using each dog's historic Sectional times.

Combined shows each dog's Calculated and Sectional bell curve Benchmark Scores in a table. It provides the average of these two scores as a combined total. You may adjust the weighting for the Calculated and Sectional benchmark score according to which you feel is the most important. For example, setting the weighting to 70 Calculated and 30 Sectional gives more importance to a dog's Calculated time when determining the overall combined score. Setting the weight to 50 : 50 gives equal importance to each score.

Benchmark Bell Curves and Score

On both the Calculated and Sectional tabs you will also find a mini bell curve visual for each dog. This visual shows you how that dog compares against three benchmark times. The calculated times mini bell curves are shown below.

Note: The curves below reflect any changes you may have made to the filters in the main bell curve at the top. If nothing is showing below, change the settings in the main visual or reload this page.

Fast. This is the average time of all the dog's fastest recent time combined.

Mean. This is the average time of all the dog's average time combined.

Slow. This is the average time of all the dog's slowest time combined.

In the top right corner of the visual you will see that dog's Benchmark Score. Higher scores are considered better than lower scores.

Filtering the Data

You can filter the data set used to generate the visual to your own preference. Changing any option will automatically reload the scatter graph. The settings will also be saved to your account automatically. You can try this yourself with the graph above (filters will not be saved to your account in this guide).

Min races

This filter sets the minimum number of races a greyhound must have participated in to be included in the visual. Any greyhound with fewer than the minimum will not generate a visual.

Max races

This filter sets the maximum number of most recent races to be included from a greyhound's data set to generate the visual. If Max races is set to 6, but the greyhound has raced 20 times previously, only the most recent 6 race times will be used.

Recency

This filter limits the data to races that occurred within a specific timeframe. Only races that have taken place within the specified timeframe will be considered. This allows you to analyse recent performance and exclude older data that might not be as relevant.

Filter by distance and...

The data filters by the same distance and then allows you to narrow down the data based on your preference. Each dog's data set will be filtered to match the current race's characteristics. Available options are: Track, Trap and Grade. For example, selecting Trap and Grade will only show data where, in previous races, each dog was in the same trap as this race and had the same grade.

Note: HP grades are not included in any scatter graph.

Scatter Choices

Through the X axis and Y axis dropdowns, choose the data you wish to be shown on the visual. You can try this on the graph above.

Calculated

The dog's historic calculated race finish time.

Sectional

The dog's historic sectional race time.

Finish

The dog's historic race finish position.

Weight

The dog's historic body weight.

Race Number

The dog's chronological historic race order. 1 is the most recent race.

First Bend

The dog's historic position in the race at the 1st bend.

Beaten By

The dog's historic number of lengths finished behind the winning dog. Dogs that won their race will have a value of 0 on the visual.

Experiment with different X and Y axis combinations to understand the power of this visual.

The Legend

On the visual's axis, is a legend that colour coordinates the visual with each different trap number. The number in the brackets is how many races from the dog's data set are being used to generate the visual. Clicking on a dog's legend, will hide the dog from the visual. Hovering the mouse cursor over a dog's legend, will highlight the dog on the visual.

NOTE: On occasion two dogs may have data plotted on exactly the same spot on a scatter graph. Only one of the dog's data dots will be visible, so it may appear the other dog is missing its data dot. You can counter this by either hiding (click on the legend) one of the dog's data so the other dog's data can now be seen, or highlight (hover mouse over legend) that dog to reveal all data dots.

The Tooltip

Moving the mouse cursor over a dot will reveal a tooltip box showing the dog's statistics.

Some example combinations

Try these with the graph above.

X: Calculated, Y: Calculated

The dogs with the fastest calculated times will have dots in the bottom left corner of the visual.

X: Calculated, Y: Sectional

This combination could help identify dogs that have the best calculated times and show consistent early pace. Look for clusters of dots in the bottom left corner.

X: First Bend, Y: Finish

This combination may help you identify dogs that start fast and maintain their lead, start fast but fade towards the end, start slow but finish strong, or start slow and cannot make up lost ground.

A cluster towards the bottom left quadrant of the visual suggests the dog tends to have a decent first bend position and can still be competitive at the end of the race with a low finish position.

A cluster around the top left quadrant suggests the dog starts well with a competitive first bend position (1st-2nd) however by the end of the race it tends to have lost positions (4th-5th).

A cluster around the top right quadrant suggests a poor history at the first bend and is unable to improve throughout the race.

A cluster around the bottom right quadrant suggests the dog starts poorly being in 4th-6th place at the first bend but has the engine to finish well, perhaps 1st-2nd position.

X: Race Number, Y: Finish

View the dog's chronological form.

X: Race Number, Y: Calculated

This combination helps you understand the dog's time trend.

X: Race Number, Y: Weight

Which is the heavier dog? If you're expecting trouble with two dogs competing at the first bend, then the heavier dog may out muscle the lighter dog. Or perhaps you know a dog performs best in a certain weight range, is it trending heavier or lighter?

Filtering the Data

You can filter the data set used to generate the visual to your own preference. Changing any option will automatically reload the box plot. The settings will also be saved to your account automatically. You can try this yourself with the box plot above (filters will not be saved to your account in this guide).

Min races

This filter sets the minimum number of races a greyhound must have participated in to be included in the visual. Any greyhound with fewer than the minimum will not generate a visual.

Max races

This filter sets the maximum number of most recent races to be included from a greyhound's data set to generate the visual. If Max races is set to 6, but the greyhound has raced 20 times previously, only the most recent 6 race times will be used.

Recency

This filter limits the data to races that occurred within a specific timeframe. Only races that have taken place within the specified timeframe will be considered. This allows you to analyse recent performance and exclude older data that might not be as relevant.

Filter by distance and...

The data filters by the same distance and then allows you to narrow down the data based on your preference. Each dog's data set will be filtered to match the current race's characteristics. Available options are: Track, Trap and Grade. For example, selecting Trap and Grade will only show curves where, in previous races, each dog was in the same trap as this race and had the same grade.

Note: HP grades are not included in any box plot.

The Legend

On the visual's axis, is a legend that colour coordinates the visual with each different trap number. The number in the brackets is how many races from the dog's data set are being used to generate the visual.

The Tooltip

Moving the mouse cursor over a trap's row will reveal a tooltip box showing a concise summary of a greyhound's recent race performance. Try it yourself with the box plot above.

Fastest

This indicates the absolute best time the greyhound has achieved in its past filtered races.

Slowest

This highlights the slowest time recorded by the greyhound.

Average

This represents the mean race time, giving you a sense of the greyhound's typical performance level.

Upper

This number is slightly more complex. It's calculated by taking the average race time and subtracting its Standard Deviation (a measure of how much those times vary). Think of it as the upper boundary of the greyhound's usual performance range.

Lower

Similar to "Upper", this is calculated by adding the Standard Deviation to the average. It represents the lower boundary of the greyhound's typical performance range.

Simulator

The Sim feature is a powerful tool designed to give you a probabilistic insight into potential race outcomes by simulating the race many times based on each greyhound's historical performance data.

Important note

This tool provides insights based on statistical simulations of historical race data. The outcomes generated are for information purposes to assist with your race profiling.

Please remember that these are simulated results and should not be the sole basis for any betting decisions. While we aim for helpful simulations, we cannot guarantee their accuracy. By using this simulator, you acknowledge that Sports-IQ is not liable for any outcomes resulting from its use. Please gamble responsibly.

How the Simulation Works

When you hit the "Run Simulation" button, the system performs 1,000,000 individual race simulations.

For each dog, the simulation begins by establishing an average time, which is determined by the historical race data filtered according to your selections (e.g., specific number of past races, track, trap, etc.). A standard deviation model is then applied to introduce realistic variations in performance for each individual simulation.

In each of the 1,000,000 simulations:

A random finishing time is generated for every dog.

68% of these simulated times will fall within plus or minus 1 standard deviation of that dog's calculated average time, representing the most common range of performance.

An additional 27% of the simulated times will fall between 1 and 2 standard deviations, accounting for less frequent but still plausible outcomes.

The remaining 5% may include rare performances that fall beyond 2 standard deviations.

These randomly generated times are then used to determine the finishing order for that specific simulation. This comprehensive process is repeated a million times, providing a robust forecast of potential race outcomes, with the results weighted by the statistical probabilities derived from the standard deviation curve.

Filtering Your Data Pre-Simulation

To ensure the simulation is based on the most relevant data, you can refine the historical races used to generate the sampled race times, before running the simulation. The settings panel, allows you to adjust:

Min races

Set a minimum number of past races a dog must have completed to be included in the data used for its simulation. The lowest setting here is 5 races.

Max races

Set the maximum number of most recent races from a greyhound's data set used in the simulation. If max races is set to 10, but the greyhound has raced 30 times previously, only the most recent 10 race times will be used.

Recency

Specify the time frame for past races from a dog's data set to be used in the simulation. You can select "All time" or choose a specific number of months from 1 to 12 months prior to the current date. This helps focus the data on recent form if desired.

Filter by distance and...

Refine the data further by selecting specific Track, Trap and/or Grade to match the race being predicted. By adjusting these filters, you can tailor the simulation to specific conditions, providing more targeted insights into potential race outcomes.

Note: HP grades are not included from the historical races.

Understanding the Results

After running the simulation, the results are presented in a table.

You will see:

Win Probability: The percentage of simulations where a particular dog finished first.

Place Probability: The percentage of simulations where a particular dog finished in the top two.

Implied Odds: These are calculated directly from the Win and Place percentages, converting the probability into a decimal odds format. Lower implied odds suggest a higher probability of that outcome occurring according to the simulation. Knowing this will allow you to identify if the odds on offer are value for your trade.

Some percentages will be coloured according to their rank:

Green indicates the dog with the highest percentage.
Yellow indicates the dog with the second highest percentage.
Red indicates the dog with the lowest percentage.

Animation

The results are displayed alongside a visual representation of each dog and their calculated win probabilities. This allows you to compare how far the percentages are apart for each dog.

During the process, the dogs will run from left to right across the screen whilst the million simulations are calculated. The dog furthest to the right represents the dog with the highest win probability and the dog furthest left represents the lowest probability.

Click the button below to see the animation.

Data Quality

The sim will only run if all dogs have sufficient data to meet the chosen filter settings criteria.

If any dog has not previously run a minimum of 5 races at the same distance as the current race, the sim will not show.

The sim is not available for HP grade races.

Start with the Performance Score

The performance score here is 96.00%, telling us it is exceptionally competitive for today's grade. The True Grade is A4 67.63% suggesting it has the ability to be competitive at a much higher level - but unlikely to win consistently at that higher level.

Analyse the Individual Races

Look at the chart to see why the scores are high. The data points all fall into zones 1 and 2 showing both consistency and the ability to run typically A6 grade winning times. Some of its races have a green up arrow indicating the dog had been performing at a higher grade. We can use the popup tooltip to understand the finer details of each race.

Form a Conclusion

The high-level scores tell us Eat A Peach looks strong, and the individual race data confirms this, showing a consistent ability to perform at a grade winning level.

Advanced Analysis: Looking Beyond the Average

The Performance Score is a powerful summary, but because it's an average of the last five runs, it's wise to look deeper. An average can be skewed by outliers - races that are unusually fast or slow.

Spot the Outlier

Scan the chart for any data point that doesn't fit the pattern. Is there one race icon positioned far to the right (an unusually slow time) that might be dragging the average score down? Or one icon far to the left (an unusually fast time) that might be inflating the score?

Investigate the "Why"

This is where the tooltip becomes essential. Hover over that outlier race and examine the in-race Comments.

A slow time might be explained by comments like "Bmpd" (Bumped) or "Crd" (Crowded). If you see this, you might mentally discount that poor performance, knowing it was due to interference and not a lack of ability. The dog's true potential is likely higher than its Performance Score suggests.

Conversely, if an exceptionally fast time has a "ClrRun" (Clear Run) comment, it tells you what the dog is capable of when it gets a clean trip. If its other, slower races were all marked with trouble comments, that fast time is likely a more accurate reflection of its true ability. The crucial question then becomes: Will it get a clear run in today's race?

By investigating why a dog was fast or slow, you move beyond the raw average and gain a much more accurate and nuanced understanding of its real performance profile.

Using The Tabs for Deeper Analysis

Calculated Tab

Use this tab to assess a dog's overall speed and finishing ability.

Sectional Tab

Switch to this tab to focus entirely on early pace. In our example race Eat a Peach is clearly a front-running threat in this grade with all five races in zone 1.

Benchmarks Tab

This tab provides a simple view of the raw benchmark times for the 6 positions in the selected grade, allowing for quick reference.