Tennis Criterias and Parameters

[Patrik Mellqvist]

I will start my corner where I will write about selections.
They are based on my thoughts and ideas.

My sources and tools are...

Tradeshark
OnCourt
Ultimate Tennis Trading
Pay Version GPT Chat
BTC Stats Software

The first I will do is make attempts to convert the comeback filter into Tennis.
That is number 1 on my to-do list at the moment.

I have five concepts to explore.
And will start with the first-set-winner strategy.

First is my own comeback filter and value scale that show promising results for soccer.
/
Second is the basic foundation criteria from the Adams Comeback Filter.
/
Third is the idea of a weak underdog with certain parameters and criteria.
/
The fourth is to explore how OnCourt's built-in Algorithm is working with the selection process / it has documentation
/
Fifth is the Tradesharks descriptions process of how to estimate in-play key factors and pre-game stats to get optimal trading opportunities regarding specific trading strategies.

Notes ...

Will explore what alternatives to soccer ELO rating alternatives for Tennis and here I will put the main focus on OnCourt and search the internet - Also read about the soccer ELO core criteria and develop my solution if there are no shortcuts.

Cheers Patrik

[Patrik Mellqvist]

Today I will show what I consider a good foundation of estimations or a raw sketch of parameters or criteria considered before the trading opportunity.

Let's say the strategy is as follows

1. I want a favorite with odds of 1.5 and below and they won more matches than underdog.

2. I want the ranking table positions to be top to middle top to bottom or middle to bottom and no other ranking positions.

3. The favorite won 60% out of 10 previous and the underdog won 70% out of 10 earlier on the same surface and would not qualify.
   However, a 70% win ratio for the favorite and a 30% or less win ratio for the underdog would give the green light for deeper analyses.

4. Now we could look into previous games' break points and comeback ratios with the model mentioned above for each game.

5. Here we can compare to soccer with no goals at the 70-minute mark to make Time Decay Value in Tennis Match and Hedge for a TIE with no loss.

I just sum it up like that and wish all the best as I have no interest in teaching when there is no interest in this topic.
I will just make the winning model and post the results of my winning trades.

Simple as that, Smile ...

Further, if you build an algorithm ... consider the following ...
Tennis Prediction Algorithm

Skills
Head to Head
Rating and countries
Current form
Tiredness and retiring
Tournament

Upon this probability calculation is made

The analysis is split into Three parts

Three Periods

In the first interval, all matches played in the last 365 Days
The second interval, 730 Days excluding the 365
In the third interval, all matches during the career excluding

[Patrik Mellqvist]

Forgot the value scale I will build with this
First Poinst - Second word description - Third explanation

This is not a complete model this is just a raw sketch of the final blueprint

-50/-35 Very weak - almost no chance avoid the match
-34/-18,5 Weak - very low tendency for comeback
-17,5/0 Less weak - a little higher tendency but still low for comeback

34/42 Cautious - possibility but being a careful estimation

43/50 Good - an average tendency for an okay match but still no guarantee
51/60 Strong - probability on our side for a comeback
61/70 Very strong - solid probability to turn the game around
71+ Extremely strong - ensure the majority to be successful

So if the underdog is with the minus field and the favorite with the plus field we have a working model based upon my writing and explanation.
This is self-explanatory and can be made with runs with GPT Chat and testing real-life matches with the pay version and giving all the instructions to build the comeback filter with both soccer and tennis.

If you need software and a coder to make this for a cheap fee just pm me.

Cheers

[Patrik Mellqvist]

Now I will spend this evening with Oncourt and the prediction algorithm.
Test and explore then pause for reflection toward tomorrow.

Focus on ELO rating and Comeback Filter Converting Mechanism to suit Tennis.

After that watch some strategies as Lay The Server and others and then let everything be alone for one or two days and then see if something new or any comprehensive solution can be found with those three components of material and knowledge.

Everything is an ongoing process.
Here are some more details about my solution for the Comeback Filter ...

Brainstorming ...
One thing I consider with the Tennis World is the top 100 and skip all the others when validating and testing the filter - this can be changed later ...

And here we have

Top Players
Middle Player
Bottom Player

Recent Performance

Anaylize the last 20 matches for general form
And the last 10 matches against similar opposition types
Top, Middle, Bottom

Also what kind of play field do they play in consideration?

Consistency Check
Evaluate how closely the data from the last 10 and 20 matches correlate
to ensure reliability.

good
avg
poor

Point values

Plus value
zero value
Negative value

for …

reliability
neutral
discreaprinies

tracking the following parameters and Point Value Estimation for each

when the favorite wins first (positive value)
when favorite equalizes (positive value)
when the favorite either concedes or strikes (positive)

when a favorite gets behind by one (negative value) common or uncommon trend
when the favorite gets behind by two (negative)

when u-dog concedes (positive)
when u-dog either strikes or coccid (positive)
when u-dog scores first (negative)
when u-dog does not concede (neutral if tie and if not negative)

Then a value model can look like this - soccer but we will convert it to tennis ...
When I have more knowledge ...

Mirassol (home team) and America (away team).
Favorite vs. U-dog

The conclusion sample shows no value in Comeback Filter Estimation with this example.

Just to show where we going with this topic ...

Step 1:
Data Preparation and Segmentation

Mirassol (Home):
• Last 20 home matches:
o Top-third teams: 7 matches
o Middle-third teams: 4 matches
o Bottom-third teams: 9 matches

America (Away):
• Last 20 away matches:
o Top-third teams: 4 matches
o Middle-third teams: 9 matches
o Bottom-third teams: 7 matches

Step 2: Identifying Comeback Instances
Mirassol (Home) Comeback Analysis:
• Comebacks:
o Instances when Mirassol fell behind 5 times.
o Comebacks (either equalizing or leading): 3 instances.

America (Away) Comeback Analysis:
• Comebacks:
o Instances when America fell behind: 6 times.
o Comebacks (either equalizing or leading): 2 instances

Step 3: Calculation of Comeback Rates
Mirassol (Home) Comeback Rate Calculation:
• Total Comebacks = 3.
• Total Instances of falling behind = 5.
• Comeback Rate = (3 / 5) * 100 = 60%.

America (Away) Comeback Rate Calculation:
• Total Comebacks = 2.
• Total Instances of falling behind = 6.
• Comeback Rate = (2 / 6) * 100 = 33.3%.

Step 4:
• Mirassol (Home) Expected Comeback
o Calculated Comeback Rate: 60%. This is below the source filter threshold, suggesting that Mirassol's actual performance in coming back from a deficit is weaker than expected based on the source model.

• America (Away) Expected Comeback Rate: 24%
o Calculated Comeback Rate: 33.3%. indicating that America performs slightly better than expected in comeback situations when away.

Step 5: Further Analysis and Considerations
• Consistency Check: Compare the last 10 vs. 20 matches for Mirassol and America to check for reliability.
o Mirassol’s performance remained consistent in both short-term (10 matches) and long-term (20 matches) datasets, which suggests a stable but modest comeback ability at home.
o America's consistency is moderate; the team showed varied results in their comeback capabilities across different opposition types, particularly against top teams.

Summary
• Mirassol (Home): The calculated comeback rate is 60%, which is significantly below the 83% threshold suggested. This suggests that Mirassol, despite some success, is not as strong in comebacks as the model expects.
• America (Away): The calculated comeback rate is 33.3%, which is above the 24% threshold set by the source, indicating America is somewhat more effective in away comebacks than anticipated.

With this, we have the Head-to-Head (H2H) Data: in the other post that is included.

Conclusion

This example shows not so strong home favorite with a good comeback ability
Show a stronger U-dog than we want and the U-dog value and strength are too high.

This is a raw sketch to be tailored to suit tennis players and tennis strategy in the future and this is my way of doing things.

Cheers

[Patrik Mellqvist]

Just some reflections on where I am going with this writing...

I am going to pick one tennis strategy with odds criteria and surface setups that suit with breakpoints where u-dog takes the lead and where you have the following scenario compared to a soccer match where a win is a win and where a draw is a win.
And where a full game with 0-1 to u-dog is a loss and a 0-2 at any time is a loss.

So following but converted to tennis

1-0 win
0-1 to 1-1 win
0-0 scratch at some time decay during the match
0-1 full time loss
0-2 take the loss at any time

Or

1-0 win
0-1 to 1-1 win
0-0 scrats at some time decay during the match
0-2 to 1-2 Scratch
0-1 full time loss
0-3 take loss at any time

[Patrik Mellqvist]

Let's forget about the comeback filter for one moment and just try another approach where you don't focus on the strong favorite at all and just make a background check from the underdog's perspective.

Then a simple point and score estimation for the value scale can look like this ...

Points Model for Evaluating Underdogs
Basic Performance (Last 20 Matches):

Loss: +3 points (positive for your strategy).
Draw: +1 point (neutral but still better than a win).
Win: -3 points (negative for your strategy).

Application: Count points for the last 20 matches. An ideal scenario would be a low point total for wins and higher points for losses.
Scenario-Specific Points:

Underdog scores first (0-1):
If the favorite equalizes (1-1): Add +2 points (instead of +1). This adjustment reflects the strength of the favorite in overcoming the initial lead, similar to a full comeback win, since equalizing under these circumstances often shows dominance over the underdog.

If the underdog concedes after leading 0-1:
Add +1 point for each goal conceded after the lead (indicating defensive weakness).
If the favorite comes back and wins 2-1: Keep this as a major positive for your strategy (+2 points).

If the underdog concedes and goes down 0-2: No additional points (indicating a significantly higher chance of loss).
Favorite scores first (1-0):
If the underdog equalizes (1-1): This is treated neutrally (0 points).
If the favorite wins after scoring first: No added points, as this is expected and not beneficial for your strategy.

Head-to-Head (H2H) Data:

Underdog has more losses or draws in H2H: +2 points (good sign).
Even H2H results: 0 points (neutral).
Underdog wins more often in H2H: -2 points (negative sign).

Number of Goals Conceded (Season or Recent Matches):

Add +0.5 points for each goal conceded per match, including matches where the underdog initially takes the lead but concedes goals to the favorite. This applies whether the game ends in a draw or loss, as conceding under these conditions shows a similar defensive weakness.

Updated Weighting System
To reflect these changes, the weighting of each parameter can remain the same, but the influence of scenario-specific points is enhanced to reflect the importance of game dynamics, especially in cases where an underdog initially leads.

Basic Performance (last 20 matches): 40% of the final score.
Scenario-Specific Points (adjusted to give more weight to equalizers and comebacks): 30% of the final score.
H2H Data: 20% of the final score.
Number of Goals Conceded: 10% of the final score (still accounting for defensive weaknesses).

New Category: Scoring Consistency and Offensive Potential
This new category evaluates the underdog's ability to score multiple goals over a series of recent matches, helping to assess their offensive strength and predict how likely they are to score 2 or more goals in future games. It reflects their overall attacking potential and consistency, adding an important layer to the analysis.

5. Scoring Consistency (Last 10 Matches):
   Core Idea: This parameter focuses on how often the underdog scores more than 1 goal in a match. If an underdog has rarely scored 2 or more goals (e.g., 3 times or fewer in the last 10 matches), it suggests inconsistency or weakness in attack, providing insight into whether they can realistically threaten a favorite's defense.
   Scoring Rules:

Underdog scores 1 or fewer goals in 70% of matches: +1 point (indicates erratic or unreliable offensive potential, not a trusted underdog for scoring many goals).
Underdog scores 1 or fewer goals in 80% of matches: +2 points (a strong signal they will struggle to score more than 1 goal, favorable for your strategy).
Underdog scores 1 or fewer goals in 90% of matches: +3 points (indicates a very high likelihood they will score 1 or no goals in the next game).
Underdog scores 1 or fewer goals in 100% of matches: +4 points (almost a bulletproof indicator that they will not score more than 1 goal, highly favorable for betting against their offensive output).
Application:

This parameter paints an overall picture of the underdog’s offensive capabilities and scoring ability. By analyzing how frequently they score more than 1 goal, you can gauge their potential to create real chances in a match and adjust your expectations accordingly. It can serve as a reliable indicator for low-scoring teams, helping you fine-tune predictions and strategies.

The problem or downside with these parameters is that the favorite will win the majority of the time.
But the upside of these parameters is when the underdog takes the lead you will win the majority of matches as a draw or comeback and maybe add a trading angle for the final win - so now we can talk and test what strategy would suit such a filter.

Reverse you can do the same value scale and estimation scale for favorite and then use both.
Or just an underdog filter?
But you can not only use the favorite filter by itself because that would be a total random indicator of how u-dog would perform.

This was the light version - after this, I will get deeper into Comeback Filter and more detail calculations based upon Adams Filter and criteria.

[Patrik Mellqvist]

This morning I decided to make some comparisons and parallels with soccer towards tennis.
It attempts to convert one working algorithm for the comeback filter from soccer to tennis.

I suggest you use the pay version of GPT Chat to fine-tune the parts I mentioned.
If you think you have a better solution.
I have cross-checked the value scale output after analyzing the same match setup with the Adams Comeback Filter and the working Pouncebet PBI bots value scale.
The last one is a working bot since 2014 using and PBI scale that is based upon the same concepts as the Adams Comeback Filter.

Reasoning from this perspective and layout is that if Ryans LTD can work for a decay and this Comeback Filter has been proven to work for a decay - I know that I sniff at value.
Because I give a ratt-s**t about strategies - because they are only empty shells with no value - and I am not into guesswork either.

When I look at parameters and criteria I have my blueprint to follow or what I consider the winning concept and are some keystones.

The following key-stones have one thing to achieve.
The best situation and underlying material before and towards the trading opportunity or match.

The following is only an illustration.

Cooperative Filter.
I want some criteria and parameters that speak and show stats that help achieve the goals and mechanism in cause and effect to get a winning trade.
For example, if I want no goals between the 10-20 min mark I want stats for the current season and with at least 8 played games 5% or fewer goals have been made between that time interval.

Neutral Filter.
This can be that I want a hint towards the degree first half has been having a 0-0 score when estimating the likelihood of goals between the 10-20 min mark and could be set to 20% or higher or any value someone thinks is a good sign - estimation.

Non Coperative Filter.
Here I want the market to think differently than me and expect goals.
Because don't have an element where the market thinks one thing and we another and better it will not become more winning trades in the long run - just following the market expectations, so with this example Under 2,5 goals can be set to 2,02 to 2,98.

Adaptive Filter.
Well in this situation a windy day or light or regular regn is a bonus for poor goals achievements with those theater conditions.
Adaptive means just that some dynamics that affect and change the conditions in real-time. Can also be Red Card the stand-up formations or key-player change during the game among other things.

Real-Time Filter.
XG is just a tailored way using in-play key stats indicators, so I use the old traditional way as it has always been working and why change, my opinion?
With the no-goal example we would have a value scale for low tempo and high temp match wish indicates depending on shots on/off target and corners to give an estimation for a goal to be likely or not likely to happen.

This is a taste of how my mindset and mentality work when setting up filters and trying to achieve a good background check of a match before the trading opportunity.

Now back to the Comeback Filter.
I am not serving things on a silverplate approach so you have to be your own master to grasp and develop things.
I will convert the following using the OnCourts Prediction Algorithms key mechanism to convert this filter into tennis.

And I don't take a no for an answer or not possible - because can a service run from 2014 with success with a comeback filter for tennis - then I know I have the mental capacity to crack the code - when Adams was so kind to give important hints about the underlying Comeback Filter.

I will read some parts of my material and pick the solution that I find valid and good to post and share in this topic as a basic foundation and material for converting into tennis and adjusting criteria and parameters to suit tennis - so some tailoring skills and creativity will be necessary.

Will update later today ...