Datasets:

ClarusC64
/

premier-league-tactical-rule-space-extraction-v0.1

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'__index_level_0__'})

This happened while the csv dataset builder was generating data using

hf://datasets/ClarusC64/premier-league-tactical-rule-space-extraction-v0.1/data/test.csv (at revision 178e29e1219c631466ab2fccf2083bd8b4ef8010), [/tmp/hf-datasets-cache/medium/datasets/59121201011111-config-parquet-and-info-ClarusC64-premier-league--c88b39ea/hub/datasets--ClarusC64--premier-league-tactical-rule-space-extraction-v0.1/snapshots/178e29e1219c631466ab2fccf2083bd8b4ef8010/data/test.csv (origin=hf://datasets/ClarusC64/premier-league-tactical-rule-space-extraction-v0.1@178e29e1219c631466ab2fccf2083bd8b4ef8010/data/test.csv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: string
              match_id: string
              team_analyzed: string
              minute_window: string
              phase_of_play: string
              context_stream_summary: double
              inferred_tactical_rules: double
              rule_trigger_conditions: double
              rule_execution_latency_seconds: double
              rule_consistency_score: double
              dominant_rule_clusters: double
              notes: string
              constraints: string
              gold_checklist: string
              __index_level_0__: string
              -- schema metadata --
              pandas: '{"index_columns": ["__index_level_0__"], "column_indexes": [{"na' + 2180
              to
              {'id': Value('string'), 'match_id': Value('string'), 'team_analyzed': Value('string'), 'minute_window': Value('string'), 'phase_of_play': Value('string'), 'context_stream_summary': Value('string'), 'inferred_tactical_rules': Value('string'), 'rule_trigger_conditions': Value('string'), 'rule_execution_latency_seconds': Value('float64'), 'rule_consistency_score': Value('float64'), 'dominant_rule_clusters': Value('string'), 'notes': Value('string'), 'constraints': Value('string'), 'gold_checklist': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'__index_level_0__'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/ClarusC64/premier-league-tactical-rule-space-extraction-v0.1/data/test.csv (at revision 178e29e1219c631466ab2fccf2083bd8b4ef8010), [/tmp/hf-datasets-cache/medium/datasets/59121201011111-config-parquet-and-info-ClarusC64-premier-league--c88b39ea/hub/datasets--ClarusC64--premier-league-tactical-rule-space-extraction-v0.1/snapshots/178e29e1219c631466ab2fccf2083bd8b4ef8010/data/test.csv (origin=hf://datasets/ClarusC64/premier-league-tactical-rule-space-extraction-v0.1@178e29e1219c631466ab2fccf2083bd8b4ef8010/data/test.csv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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id string	match_id string	team_analyzed string	minute_window string	phase_of_play string	context_stream_summary string	inferred_tactical_rules string	rule_trigger_conditions string	rule_execution_latency_seconds float64	rule_consistency_score float64	dominant_rule_clusters string	notes string	constraints string	gold_checklist string
TRSE-001	EPL-2025-210	MCI	12-18	transition_defense	loss in final third twice; nearest 5 compress; regain once	R1: if loss in final third then counter-press with front 5 for 7s	loss_zone=final_third;time_since_loss<=2s	2.1	0.82	counter_press	Rule repeats across both losses	Premier League only.	rules+triggers+latency+clusters
TRSE-002	EPL-2025-211	ARS	24-30	build_to_midfield	press beaten centrally once; CB steps and FB tucks; next build repeats	R2: if central press line broken then CB steps and far FB tucks to protect rest-defense	press_break_location=central;ball_speed_upfield=high	1.8	0.76	rest_defense_rotation	Rotation looks consistent	Premier League only.	rules+triggers+latency+clusters
TRSE-003	EPL-2025-212	LIV	40-46	defensive_box_protection	opponent overload wide; far 8 sprints to box twice; cutback blocked once	R3: if wide overload threatens cutback then far 8 drops into box lane	overload_side=wide;cutback_threat=true	2.6	0.7	box_protection	Late arrival on second instance	Premier League only.	rules+triggers+latency+clusters
TRSE-004	EPL-2025-213	TOT	55-61	direct_defense	opponent plays long to target; line retreats; duel contested; second ball lost	R4: if long ball to targetman then back line retreats and contests aerial duel	ball_type=long;target_receipt=central	3	0.68	direct_defense	Retreat timing slow	Premier League only.	rules+triggers+latency+clusters
TRSE-005	EPL-2025-214	NEW	18-24	shape_reset	turnover in middle third; team regroups into mid-block within 3s	R5: if turnover in middle third then reset into mid-block before next press	turnover_zone=middle_third;opponent_progression=moderate	2.4	0.74	shape_reset	Reliable regroup pattern	Premier League only.	rules+triggers+latency+clusters
TRSE-006	EPL-2025-215	CHE	30-36	between_lines_press	10 receives between lines twice; nearest 2 jump; forced back pass	R6: if 10 receives between lines then double-jump press with nearest 2	receiver_role=10;receive_zone=between_lines	1.6	0.8	zone_press	Fast jump on both events	Premier League only.	rules+triggers+latency+clusters

What this dataset tests

Whether a system can infer an opponent’s rule-space as explicit if-then tactical rules.

This is not formation spotting. This is behavior law extraction.

Core task

Given a match window summary infer

the trigger condition
the coordinated response
reaction latency in seconds
consistency score
the rule cluster

Required outputs

inferred_tactical_rules
rule_trigger_conditions
rule_execution_latency_seconds
rule_consistency_score
dominant_rule_clusters

Example rule format

R1: if loss in final third then counter-press with front 5 for 7s
triggers like loss_zone=final_third and time_since_loss<=2s

Constraints

Premier League only
rules must be extracted from the window context
avoid generic talk like "they press high" unless you express a rule

Evaluation focus

High scores require

clear if-then structure
trigger fields in key=value form
a latency value
a cluster label that matches the rule type

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