# Player Rating Aggregator AI Agent Connect

> Player Rating Aggregator provides your AI client with the mathematical tools needed to analyze football player performance. It handles complex calculations like weighted averages and rating distributions, allowing you to move past simple scores to understand true player trends and consistency.

## Overview
- **Category:** statistics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_jqUb482KMi9KAIEvkqo4C0ZnGcidaymKSIyO4jSt/ai-agent-connect
- **Tags:** football, soccer, player-stats, performance, analytics

## Description

This MCP gives your agent the ability to process football player statistics with mathematical precision. Instead of looking at a single score, you can use it to build a complete statistical profile for any player. You can calculate averages, medians, and standard deviations to see how much a player's performance fluctuates. If you need to see if a player is currently in form or hitting a slump, the tool tracks performance trends over specific timeframes. You can also dig into the raw data by pulling chronological match histories or looking at rating distributions to see how often a player hits high or low marks. It's built for anyone who needs to turn match-by-match ratings into actionable scouting or performance data without doing the math manually.

## Tools

### get_player_match_history
This tool pulls a chronological list of every match rating recorded for a specific player.

### get_player_performance_trend
This tool identifies if a player's performance is currently improving, declining, or staying stable.

### get_player_rating_distribution
This tool breaks down how frequently a player receives specific rating ranges, such as how often they score an 8 or above.

### get_player_rating_summary
This tool generates a statistical profile including averages and standard deviations for a player over a set period.

## Prompt Examples

**Prompt:** 
```
Give me a statistical summary of player 'p123' from 2023-01-01 to 2023-12-31 using arithmetic average.
```

**Response:** 
```
For player p123 in 2023, the average rating was 7.2, with a median of 7.0, a minimum of 5.5, a maximum of 8.5, and a standard deviation of 0.8 across 25 matches.
```

**Prompt:** 
```
Is player 'p456' performing better or worse lately? Compare Jan 2024 to Dec 2023.
```

**Response:** 
```
Player p456 is improving. The current period average is 7.8 compared to the previous period average of 7.2, showing an upward trend of 0.6.
```

**Prompt:** 
```
Show me the match history for player 'p789' for the first half of 2024.
```

**Response:** 
```
Player p789 played 12 matches between 2024-01-01 and 2024-06-30. The ratings ranged from 6.0 to 8.0, with the most recent match on 2024-06-15 resulting in a 7.5.
```

## Capabilities

### Statistical Profiling
Your agent calculates averages, medians, and standard deviations for player ratings.

### Trend Analysis
The MCP identifies whether a player is on an upward or downward performance trajectory.

### Frequency Distribution
Your client can see how often a player hits specific rating thresholds.

### Chronological Tracking
The tool retrieves sorted match histories to map performance over time.

## Use Cases

### Scouting Consistency
Check a player's rating distribution to see if they are a steady performer or highly volatile.

### Form Tracking
Use trend analysis to determine if a player is currently in peak form before a big match.

### Historical Audits
Pull full match histories to review a player's performance over an entire season.

### Statistical Reporting
Generate summary profiles with medians and deviations for team performance reviews.

## Benefits

- Automates complex math like weighted averages and standard deviations.
- Identifies performance shifts through trend detection.
- Provides a clear view of player consistency via rating distributions.
- Organizes match data into usable chronological lists.

## How It Works

Connecting this MCP to your AI client gives your agent immediate access to football statistical tools.

1. Connect your preferred MCP client to Vinkius.
2. Select the Player Rating Aggregator from the catalog.
3. Ask your AI client to perform a specific statistical task or query.
4. The MCP executes the math or data retrieval and returns the results to your agent.

## Frequently Asked Questions

**What kind of math does this MCP perform?**
It handles arithmetic and weighted averages, as well as calculating medians and standard deviations for player ratings.

**Can I see if a player is improving?**
Yes, you can use the performance trend tool to see if a player's ratings are trending up, down, or staying stable.

**Which AI clients can use this?**
You can use this MCP with any compatible client like Claude, Cursor, Windsurf, or VS Code.

**How do I see a player's specific match scores?**
You can use the match history tool to get a sorted list of all ratings for a specific player.

**Does this work for soccer and football?**
The tool is designed for football and soccer player performance metrics.

**What aggregation methods are supported?**
The server supports arithmetic mean, weighted average (using a custom weight map), and minutes-weighted average which accounts for time played.

**How can I see if a player's form is improving?**
You can use the `get_player_performance_trend` tool to compare the average rating of a current period against a previous one.

**Can I handle matches where no rating was given?**
Yes, you can specify a missing rating policy (ignore or baseline) when calling `get_player_rating_summary`.