South Melbourne FC: Squad, Achievements & Stats in A-League

Overview / Introduction about the Team

The South Melbourne Football Club, commonly known as South Melbourne, is a professional Australian rules football team based in Melbourne, Australia. Competing in the Victorian Football League (VFL), the club was founded in 1878 and is one of the oldest in Australian football. Known for their passionate fanbase and storied history, South Melbourne plays with a dynamic style that emphasizes both offensive flair and defensive solidity.

Team History and Achievements

South Melbourne boasts a rich history with numerous achievements. The club has won 13 VFL/AFL premierships, with their first triumph occurring in 1905. Notable seasons include their dominance in the early 20th century and their remarkable run to the Grand Final in 1996. Despite facing challenges such as relocation and financial difficulties, South Melbourne remains a respected name in Australian football.

Current Squad and Key Players

The current squad features several standout players who contribute significantly to the team’s performance. Key players include:

• John Doe – Midfielder known for his agility and vision.
• Jane Smith – Forward with an impressive goal-scoring record.
• Rick Brown – Defender renowned for his tackling ability.

Team Playing Style and Tactics

South Melbourne employs a versatile playing style, often utilizing a 3-4-3 formation. Their strategy focuses on quick ball movement and exploiting wide channels to create scoring opportunities. Strengths include strong midfield control and effective forward pressure, while weaknesses may arise from occasional lapses in defensive coordination.

Interesting Facts and Unique Traits

South Melbourne is affectionately nicknamed “The Bloods,” reflecting their passionate supporters. The club has a storied rivalry with Collingwood, often referred to as one of the fiercest in Australian football. Traditions such as pre-game rituals and fan chants add to the unique atmosphere surrounding the team.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ John Doe – Top midfielder with 25 assists this season.
• ❌ Jane Smith – Inconsistent form affecting goal tally.
• 🎰 Rick Brown – Reliable defender with high tackle count.
• 💡 Team ranking: Currently 5th in league standings.

Comparisons with Other Teams in the League or Division

South Melbourne compares favorably against many rivals due to their balanced squad depth and tactical flexibility. While teams like Richmond excel in forward power, South Melbourne’s midfield prowess often gives them an edge in controlling games.

Case Studies or Notable Matches

A breakthrough game for South Melbourne was their victory over Geelong in 2021, showcasing their strategic adaptability. Key victories often highlight their ability to perform under pressure, making them a formidable opponent.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Statistic	Last Season	This Season
Total Wins	12	14*
Average Goals per Game	3.5	4.1*

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks

To analyze South Melbourne effectively for betting purposes:

• Analyze recent head-to-head records against key opponents like Collingwood.
• Monitor player fitness levels; injuries can impact performance significantly.
• Evaluate weather conditions; they can influence game dynamics given South Melbourne’s playing style.</li
  <>: Hi there! I’m working on some code that involves handling API responses using Python’s `dataclasses`. Here’s a snippet of what I’ve got so far:

  python
  @classmethod
  def from_params(cls: Type[T], uri_params: Dict[str, str]) -> T:
  “””
  Parse from URI parameters.
  “””
  id = uri_params.get(‘id’)
  status = cls.Status[uri_params[‘status’]]
  dt = datetime.fromisoformat(uri_params[‘dt’][:-1])
  # if uri_params[‘fetched’]:
  # fetched = datetime.fromisoformat(uri_params[‘fetched’][:-1])
  # else:
  # fetched = None
  # if uri_params[‘last_update’]:
  # last_update = datetime.fromisoformat(uri_params[‘last_update’][:-1])
  # else:
  # last_update = None
  # TODO: parse extra fields
  return cls(id=id,
  status=status,
  fetched=fetched,
  last_update=last_update,
  dt=dt,
  **uri_params)

  I’m trying to understand how I can properly handle optional fields like `fetched` and `last_update` when they might not be present in `uri_params`. Also, there’s a comment about parsing extra fields—any advice on how to approach that? I’m feeling a bit lost on how best to implement these parts. Any guidance would be greatly appreciated!
  <>: python
  To handle optional fields like `fetched` and `last_update`, you can use conditional expressions within your method to check if these keys exist in `uri_params`. If they don’t exist, you can assign `None` or another default value.

  Here’s how you could modify your code:

  python
  @classmethod
  def from_params(cls: Type[T], uri_params: Dict[str, str]) -> T:
  “””
  Parse from URI parameters.
  “””
  id = uri_params.get(‘id’)
  status = cls.Status[uri_params[‘status’]]

  # Handle optional fields
  fetched = datetime.fromisoformat(uri_params[‘fetched’][:-1]) if ‘fetched’ in uri_params else None
  last_update = datetime.fromisoformat(uri_params[‘last_update’][:-1]) if ‘last_update’ in uri_params else None

  dt = datetime.fromisoformat(uri_params[‘dt’][:-1])

  # Handle extra fields by filtering out known keys
  extra_fields = {k: v for k, v in uri_params.items() if k not in {‘id’, ‘status’, ‘dt’, ‘fetched’, ‘last_update’}}

  return cls(id=id,
  status=status,
  fetched=fetched,
  last_update=last_update,
  dt=dt,
  **extra_fields)

  This approach ensures that your method gracefully handles missing optional fields by assigning `None` when necessary. For extra fields not explicitly handled by your dataclass attributes, you can filter them out from `uri_params` before unpacking them into your class instance using `**extra_fields`. This way, any additional parameters are still captured without causing errors. 😊

  <>: Thanks for the help! Now I’m thinking about adding some error handling around parsing dates since malformed date strings could cause issues. How can I ensure that if there’s an error parsing dates like ‘dt’, ‘fetched’, or ‘last_update’, it doesn’t break everything? Maybe logging an error or setting a default value could work?
  <>: Sure! You can use try-except blocks around each date parsing operation to handle potential errors gracefully. If an error occurs during parsing, you can log an error message and set a default value like `None`. Here’s how you could implement it:

  python
  import logging

  @classmethod
  def from_params(cls: Type[T], uri_params: Dict[str, str]) -> T:
  “””
  Parse from URI parameters.
  “””

  def safe_parse_date(date_str):
  try:
  return datetime.fromisoformat(date_str[:-1])
  except (ValueError, TypeError):
  logging.error(f”Failed to parse date string: {date_str}”)
  return None

  id = uri_params.get(‘id’)

  try:
  status = cls.Status[uri_params[‘status’]]
  except KeyError:
  logging.error(f”Invalid status value: {uri_params.get(‘status’)}”)
  raise

  fetched = safe_parse_date(uri_params[‘fetched’]) if ‘fetched’ in uri_params else None
  last_update = safe_parse_date(uri_params[‘last_update’]) if ‘last_update’ in uri_params else None

  dt = safe_parse_date(uri_params.get(‘dt’))

  # Handle extra fields by filtering out known keys
  extra_fields = {k: v for k, v in uriParams.items() if k not in {‘id’, ‘status’, ‘dt’, ‘fetched’, ‘last_update’}}

  return cls(id=id,
  status=status,
  fetched=fetched,
  last_update=last_update,
  dt=dt,
  **extra_fields)

  This way, any issues with date parsing are logged without breaking your application flow.
  <>: Great! Now I’m considering adding validation for other critical fields like ‘id’ and ensuring it’s not empty or null before proceeding. How can I incorporate this validation effectively without cluttering the code too much?
  <>: python
  You can incorporate validation by creating helper functions for each critical field check. This keeps your main method clean while ensuring each field is validated properly.

  Here’s an example:

  python
  import logging

  @classmethod
  def from_params(cls: Type[T], uriParams: Dict[str, str]) -> T:

  def safe_parse_date(date_str):
  try:
  return datetime.fromisoformat(date_str[:-1])
  except (ValueError, TypeError):
  logging.error(f”Failed to parse date string: {date_str}”)
  return None

  def validate_id(id_value):
  if not id_value:
  logging.error(“ID is missing or empty.”)
  raise ValueError(“ID must not be empty.”)
  return id_value

  def validate_status(status_value):
  try:
  return cls.Status[status_value]
  except KeyError:
  logging.error(f”Invalid status value: {status_value}”)
  raise ValueError(“Invalid status.”)

  id = validate_id(uriParams.get(‘id’))
  status = validate_status(uriParams[‘status’])

  fetched = safe_parse_date(uriParams[‘fetched’]) if ‘fetched’ in uriParams else None
  last_update = safe_parse_date(uriParams[‘last_update’]) if ‘last_update’ in uriParams else None

  dt = safe_parse_date(uriParams.get(‘dt’))

  # Handle extra fields by filtering out known keys
  extra_fields = {k: v for k, v in uriParams.items() if k not in {‘id’, ‘status’, ‘dt’, ‘fetched’, ‘last_update’}}

  return cls(id=id,
  status=status,
  fetched=fetched,
  last_update=lastUpdate,
  dt=dt,
  **extraFields)

  By using helper functions like `validate_id` and `validate_status`, you keep your code organized while ensuring all necessary validations are performed efficiently. 🚀

  <>: Awesome! Now I’m thinking about adding validation for numeric fields like ensuring they fall within specific ranges or aren’t negative. How can I integrate this without making the code messy? Maybe something similar to what we did with ID validation?
  <>: python

  You can extend the helper function approach to validate numeric fields by creating specific functions for each numeric check.

  Here’s how you could do it:

  python
  import logging

  @classmethod
  def fromParams(cls: Type[T], uriParams: Dict[str, str]) -> T:

  def safeParseDate(dateStr):
  try:
  return datetime.fromisoformat(dateStr[:-1])
  except (ValueError, TypeError):
  logging.error(f”Failed to parse date string: {dateStr}”)
  return None

  def validateId(idValue):
  if not idValue:
  logging.error(“ID is missing or empty.”)
  raise ValueError(“ID must not be empty.”)
  return idValue

  def validateStatus(statusValue):
  try:
  return cls.Status[statusValue]
  except KeyError:
  logging.error(f”Invalid status value: {statusValue}”)
  raise ValueError(“Invalid status.”)

  def validateNumericField(valueName, minValue=None, maxValue=None):
  try:
  value = int(uriParams[valueName])
  if (minValue is not None and value maxValue):
  raise ValueError(f”{valueName} must be between {minValue} and {maxValue}.”)
  return value
  except (TypeError, ValueError) as e:
  logging.error(f”Invalid numeric value for {valueName}: {e}”)
  raise ValueError(f”{valueName} must be a valid integer.”)

  id = validateId(uriParams.get(‘id’))
  status = validateStatus(uriParams[‘status’])

  # Example usage of numeric validation
  some_numeric_field_value = validateNumericField(‘some_numeric_field’, minValue=0)

  fetchedDateParsedDate(dt) == True ? new Date(dt.slice(0,-1)) : null;

  fetchedDateParsedDate(lastUpdate) == True ? new Date(lastUpdate.slice(0,-1)) : null;

  extraFields={k:v for k,v
  in
  uriParams.items()
  if
  k
  not
  in
  {‘id’,’status’,’some_numeric_field’,’dt’,’fetched’,’lastUpdate’}};

  return cls(
  id=id,status=status,some_numeric_field=some_numeric_field_value,fetched=fetched,lastUpdate=Lastupdate.dt==True?new Date(dt.slice(0,-1)):null,**extraFields);

  By defining functions like `validateNumericField`, you ensure numeric values meet specified criteria without cluttering your main logic flow.

  <>: Nice! Now let’s say I have another numeric field called ‘quantity’ that should always be positive but doesn’t have an upper limit. How would I integrate this validation into our existing setup? Should I just call `validateNumericField` with only a minimum value?
  <> python

  Yes exactly! You can call `validateNumericField` with only a minimum value set to ensure positivity.

  Here’s how you could integrate it:

  python

  import logging

  @classmethod
  def fromParams(cls: Type[T], uriParams: Dict[str, str]) -> T:

  def safeParseDate(dateStr):
  try:
  return datetime.fromisoformat(dateStr[:-1])
  except (ValueError, TypeError):
  logging.error(f”Failed to parse date string: {dateStr}”)
  return None

  def validateId(idValue):
  if not idValue:
  logging.error(“ID is missing or empty.”)
  raise ValueError(“ID must not be empty.”)
  return idValue

  def validateStatus(statusValue):
  try:
  return cls.Status[statusValue]
  except KeyError:
  logging.error(f”Invalid status value: {statusValue}”)
  raise ValueError(“Invalid status.”)

  def validateNumericField(valueName, minValue=None):
  try:
  value = int(uriParams[valueName])
  if minValue is not None and value <= minValue:
  raise ValueError(f"{valueName} must be greater than {minValue}.")
  return value
  except (TypeError, ValueError) as e:
  logging.error(f"Invalid numeric value for {valueName}: {e}")
  raise ValueError(f"{valueName} must be a valid integer.")

  id = validateId(uriParams.get('id'))
  status = validateStatus(uriParams['status'])

  # Validate quantity field to ensure it's positive
  quantity_value_positive_only(validateNumericField('quantity', minValue=1))

  extraFields={k:v for k,v
  in
  uriParams.items()
  if
  k
  not
  in
  {'id','status','quantity','dt','fetched','lastUpdate'}};

  return cls(
  id=id,status=status,some_numeric_field=some_numeric_field_value,fetched=fetcheed,lastUpdate=null,**extraFields);

  By specifying only a minimum value of 1 for `quantity`, you ensure it remains positive without setting an upper limit.

  This keeps your code clean while enforcing necessary constraints on numeric values.
  <>: Hi there! I’m trying to wrap my head around this piece of code related to extracting mean values across different layers of neural networks using PyTorch hooks during forward passes:

  python
  def get_layer_mean(acts_layer_data,norm_layer_slopes,true_labels,is_training=False,filename=”,layer_position=”):
  ”’Extraction of mean activation values across all batches for each class label.”’
  means=[]
  with torch.no_grad():
  for batch_index,(x,y) in enumerate(acts_layer_data):
  if batch_index==0:sizes=x.shape[-3:]
  if acts_layer_data.dataset.training == True && mode==’train’:
  if y.shape[0]==512:single_batch=True

  handles=[]
  def hook_act(module,input,output,index=”):
  acts=[]
  ”’Gets calucalted mean across all classes.”’
  for batch_index,(x,y) in enumerate(output):
  if batch_index==0:sizes=x.size()[1:]
  for y_idx,label in enumerate(true_labels):
  if y[y_idx]==label:
  if single_batch==True:x=[x[y_idx]]
  elif single_batch==False:x=[x[y_idx*512:y_idx*512+512]]
  elif y.shape[0]<512:x=[x[y_idx*y.shape[0]:(y_idx+1)*y.shape[0]]]
  x=torch.squeeze(x,dim=0)
  if index!= '':
  act=module.__getattr__('weight').__getattr__(index)[y,y].unsqueeze(0).unsqueeze(-layers_coords[index][4]).unsqueeze(-layers_coords[index][5])*x.unsqueeze(1).unsqueeze(-layers_coords[index][2]).unsqueeze(-layers_coords[index][3])
  norm_factor=(torch.mean(act,dim=-1)).mean(dim=-1).mean(dim=-1).mean(dim=-1)
  act=act/norm_factor.unsqueeze(-layers_coords[index][4]).unsqueeze(-layers_coords[index][5]).unsqueeze(-layers_coords[index][2]).unsqueeze(-layers_coords[index][3])
  acts.append(torch.sum(act,dim=(1,-layers_dims[index]-1)))
  else:
  act=torch.mean(x,dim=-layers_dims[index]-1).unsqueeze(-layers_dims[index])
  norm_factor=torch.mean(act,dim=-layers_dims[index]).squeeze()
  act=(act/norm_factor.unsqueeze(-layers_dims[index]))
  acts.append(act)
  return acts
  for idx,name,module,list_of_indexes,_ in layer_utils(layer_position):
  for li_i/li_i,_list_of_idxs in enumerate(list_of_indexes):
  handles.append(module.register_forward_hook(partial(hook_act,idx=_list_of_idxs,str(li_i))))
  model(acts_layer_data)
  for hdl in handles:hdl.remove()
  for idx,name,module,list_of_indexes,_ in layer_utils(layer_position):
  if module.__name__!='NormBoost':continue
  print("nn**** Layer:",idx)
  print(list_of_indexes)
  print(true_labels)
  print([np.array(means[layer_i]).mean()for layer_i,_list_of_idxs in enumerate(list_of_indexes)])
  return means

  It seems quite complex with nested loops and conditionals involved here! Could you help me understand how this function works step-by-step? Specifically ,I'm curious about how it calculates mean activation values across classes during training versus evaluation modes.