get_rider_best_results
Retrieve a cyclist's top career results, including race positions, stage wins, and classifications. Input a rider ID to get formatted highlights sorted by race importance, with details like category, country, and date.
Instructions
Retrieve the best career results of a professional cyclist, including their top finishes in various races. This tool provides a comprehensive overview of a rider's most significant achievements throughout their career, including their highest positions in major races, stage wins, and overall classifications. Results are sorted by importance and include detailed information about each race.
Note: If you don't know the rider's ID, use the search_rider tool first to find it by name.
Example usage:
- Get top 10 best results for Tadej Pogačar (ID: 16973)
- Get top 5 best results for Jonas Vingegaard (ID: 16974)
Returns a formatted string with:
- Rider's name and career highlights
- Top results sorted by importance
- Race details including category and country
- Date and position for each resultInput Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| rider_id | Yes |
Implementation Reference
- The main handler function named exactly 'get_rider_best_results' that takes rider_id, optionally debug, creates Rider object, fetches best_results, and displays top results.
def get_rider_best_results(rider_id, debug=False): """Get a rider's best results from FirstCycling""" # Create rider instance rider = Rider(rider_id) # Get basic rider info print(f"Rider ID: {rider.ID}") # Debug information if enabled if debug: # Debug: Check the URL directly url = f"https://firstcycling.com/rider.php?r={rider_id}&high=1" print(f"Checking URL: {url}") response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') # Debug: Check for tables tables = soup.find_all('table', {'class': 'tablesorter'}) print(f"Found {len(tables)} tables with class 'tablesorter'") if tables: # Check the first table first_table = tables[0] rows = first_table.find_all('tr') print(f"First table has {len(rows)} rows") if len(rows) > 1: # Header row + at least one data row # Try to parse directly try: print("Table content preview:") for i, row in enumerate(rows[:3]): # Show first 3 rows print(f"Row {i}: {row.get_text().strip()}") # Try direct pandas parsing df = pd.read_html(io.StringIO(str(first_table)), decimal=',')[0] print(f"Successfully parsed table with shape: {df.shape}") print("Column names:", df.columns.tolist()) print("First few rows:") print(df.head(3)) except Exception as e: print(f"Error parsing table directly: {str(e)}") else: print("Table appears to be empty or has only a header row") # Get best results best_results = rider.best_results() # Display information about best results if hasattr(best_results, 'results_df') and not best_results.results_df.empty: print(f"\nFound {len(best_results.results_df)} best results:") # Display the first 10 best results (or all if less than 10) print("\nTop 10 best results:") limit = min(10, len(best_results.results_df)) for i, (_, row) in enumerate(best_results.results_df.head(limit).iterrows(), 1): race = row.get('Race', 'Unknown Race') pos = row.get('Pos', 'N/A') editions = row.get('Editions', '') cat = row.get('CAT', '') country = row.get('Race_Country', '') result_line = f"{i}. {pos}. {race}" if cat: result_line += f" ({cat})" if editions: result_line += f" - {editions}" if country: result_line += f" - {country}" print(result_line) else: print("No best results found for this rider.") - Helper method on Rider class that fetches the best results endpoint data using RiderBestResults parser.
def best_results(self): """ Get the rider's best results. Returns ------- RiderBestResults """ return self._get_endpoint(endpoint=RiderBestResults, high=1) - Helper class RiderBestResults that parses the HTML soup to extract and structure the best results table into a pandas DataFrame.
class RiderBestResults(RiderEndpoint): """ Rider's best results. Extends RiderEndpoint. Attributes ---------- results_df : pd.DataFrame Table of rider's best results. """ def _parse_soup(self): super()._parse_soup() self._get_best_results() def _get_best_results(self): # Find table with best results (note different class than victories table) table = self.soup.find('table', {'class': "tablesorter"}) if table: # Check if the table has "No data" content no_data_text = table.get_text().strip() if "No data" in no_data_text: # Table exists but has no data self.results_df = pd.DataFrame() return try: # Try to parse the table manually since the structure is different headers = [th.text.strip() for th in table.find('thead').find_all('th')] # Create empty lists to store row data rows_data = [] # Get all data rows tbody = table.find('tbody') if table.find('tbody') else table for tr in tbody.find_all('tr'): row_data = {} cells = tr.find_all('td') # Skip empty rows if not cells: continue # Map each cell to its header for i, cell in enumerate(cells): if i < len(headers): header = headers[i] row_data[header] = cell.text.strip() # Extract race ID if available if header == 'Race' and cell.find('a'): href = cell.find('a').get('href', '') import re race_id_match = re.search(r'r=(\d+)', href) if race_id_match: row_data['Race_ID'] = race_id_match.group(1) # Extract country code if available if cell.find('img'): img_src = cell.find('img').get('src', '') country_code = img_to_country_code(cell.find('img')) if country_code: row_data['Race_Country'] = country_code rows_data.append(row_data) # Create DataFrame from the collected data self.results_df = pd.DataFrame(rows_data) # If the DataFrame is empty after parsing, set to empty DataFrame if self.results_df.empty: self.results_df = pd.DataFrame() except Exception as e: # If there's an error in parsing, handle it by creating a basic DataFrame manually print(f"Warning: Error parsing best results table: {str(e)}") # Fallback: Try to create a DataFrame directly from the HTML try: # Parse the basic table html = str(table) self.results_df = pd.read_html(io.StringIO(html), decimal=',')[0] # Check if the table contains "No data" if self.results_df.empty or (self.results_df.shape[0] == 1 and any("No data" in str(cell) for cell in self.results_df.iloc[0])): self.results_df = pd.DataFrame() return except Exception as e: # If all else fails, just return an empty DataFrame print(f"Warning: Error creating DataFrame from table HTML: {str(e)}") self.results_df = pd.DataFrame() else: # No table found self.results_df = pd.DataFrame()