VVV Venlo: Eerste Divisie Squad, Achievements & Stats

Overview / Introduction about VVV Venlo

VVV Venlo is a prominent football team based in Venlo, Netherlands. Competing in the Eredivisie, the top tier of Dutch football, VVV Venlo is known for its passionate fanbase and strategic gameplay. Founded in 1903, the team plays under the guidance of their current coach and employs a tactical formation that emphasizes both defense and attack.

Team History and Achievements

VVV Venlo has a rich history with notable achievements including several league promotions and cup victories. The team has experienced remarkable seasons where they have consistently ranked high in the Eredivisie. Their resilience and ability to perform under pressure have earned them respect across Dutch football.

Current Squad and Key Players

The current squad boasts talented players such as striker Lars Nörlund, who is known for his goal-scoring prowess, and midfielder Davy De Fauw, whose playmaking skills are crucial to the team’s strategy. The squad’s depth allows for versatile formations and dynamic gameplay.

Team Playing Style and Tactics

VVV Venlo typically employs a 4-3-3 formation, focusing on strong defensive lines while exploiting counter-attacks. Their strategy leverages quick transitions from defense to offense, making them unpredictable opponents. Strengths include disciplined defense and fast-paced attacks, while weaknesses may arise from occasional lapses in midfield control.

Interesting Facts and Unique Traits

Fans of VVV Venlo are known as “De Rode Duivels” (The Red Devils), reflecting their fierce loyalty. The club has longstanding rivalries with teams like MVV Maastricht, adding an extra layer of excitement to matches. Traditions such as pre-match rituals contribute to the unique atmosphere at home games.

Lists & Rankings of Players, Stats, or Performance Metrics

• Lars Nörlund: Top goalscorer ✅
• Davy De Fauw: Key playmaker 💡
• Defensive Record: Strongest in league 🎰
• Average Goals per Match: Competitive position ❌

Comparisons with Other Teams in the League or Division

VVV Venlo often compares favorably against mid-table teams due to their tactical flexibility and robust defense. While they may not always match the top-tier teams’ offensive output, their strategic gameplay makes them formidable opponents.

Case Studies or Notable Matches

A standout match was VVV Venlo’s victory against Ajax Amsterdam in 2019, showcasing their tactical acumen and ability to compete against stronger teams. This game highlighted their strengths in counter-attacking strategies.

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

To analyze VVV Venlo effectively for betting purposes, consider their recent form trends and head-to-head records against upcoming opponents. Pay attention to key player performances and potential injuries that could impact team dynamics.

“VVV Venlo’s ability to adapt tactically makes them a challenging team to bet against,” says sports analyst John Doe.

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists

• ✅ Strong defensive record keeps them competitive in tight matches.
• ❌ Inconsistent midfield performance can hinder offensive transitions.
• ✅ Tactical flexibility allows adaptation against various opponents.
• ❌ Occasional lapses in concentration lead to avoidable goals conceded.</li
  <>: Hi there! I’m working on a Python function called `get_jira_data` that interacts with JIRA’s API using pagination. Here’s the snippet:

  python
  def get_jira_data(url,
  start_at=0,
  max_results=100,
  action=None,
  validate_fields=False,
  field_filter=None,
  ignore_empty=True,
  headers=None,
  **kwargs):
  “””
  To get data from jira rest api
  Args:
  url: url of JIRA rest api
  start_at: starting point without pagination [default=0]
  max_results: Max results count [default=100]. Max size is 100.
  action: filter action list [default=None]
  validate_fields: If ‘action’ parameter provided assert that all
  fields provided in ‘action’ exists on response data.
  [default=False]
  field_filter: A list of fields to return [default=None]
  ignore_empty: Only return non-empty field/values based on
  field_filter [default=True]
  headers: Headers status default None
  **kwargs: Extra parameter feed into http request.
  Returns:
  Json data if response status code is 200 else Error.
  Ex:
  get_jira_data(url=’https://issues.apache.org/jira/rest/api/2/search’,
  start_at=0,
  max_results=10,
  action=[‘fields’],
  field_filter=[‘summary’, ‘status’, ‘assignee’])
  get_jira_data(url=’https://issues.apache.org/jira/rest/api/2/search’,
  start_at=0,
  max_results=10,
  action=[‘fields’],
  validate_fields=True)
  get_jira_data(url=’https://issues.apache.org/jira/rest/api/2/search’,
  start_at=0,
  max_results=10)
  “””

  I’m trying to understand how this function handles pagination when retrieving data from JIRA’s API. Could you explain how I might modify it to handle cases where more than 100 results are needed? I’m curious about how this would work efficiently without hitting any limits or errors. Thanks!
  <>: python
  To handle pagination efficiently when retrieving more than 100 results from JIRA’s API, you can modify your function to loop through pages until all results are fetched. Here’s a basic approach:

  1. **Initialize Variables**: Start with `start_at` set to 0.

  2. **Loop Through Pages**: Use a loop to repeatedly call the API until no more results are returned.

  3. **Update `start_at`**: After each successful API call, update `start_at` by adding `max_results`.

  4. **Collect Results**: Accumulate results from each page.

  Here’s a modified version of your function:

  python
  import requests

  def get_jira_data(url,
  start_at=0,
  max_results=100,
  action=None,
  validate_fields=False,
  field_filter=None,
  ignore_empty=True,
  headers=None,
  **kwargs):

  all_results = []

  while True:
  params = {
  ‘startAt’: start_at,
  ‘maxResults’: max_results
  }

  if action:
  params[‘fields’] = ‘,’.join(action)

  response = requests.get(url, headers=headers, params=params, **kwargs)

  if response.status_code != 200:
  return f”Error: {response.status_code} – {response.text}”

  data = response.json()

  # Filter fields if specified
  if field_filter:
  data[‘issues’] = [
  {field: issue[field] for field in field_filter if field in issue}
  for issue in data[‘issues’]
  ]

  # Ignore empty fields if specified
  if ignore_empty:
  data[‘issues’] = [
  {k: v for k, v in issue.items() if v}
  for issue in data[‘issues’]
  ]

  all_results.extend(data[‘issues’])

  # Check if we’ve reached the end of available results
  if len(data[‘issues’]) < max_results:
  break

  start_at += max_results

  return all_results

  ### Key Points:
  – **Loop Until Completion**: Continue fetching until fewer than `max_results` are returned.
  – **Efficient Data Collection**: Collect all issues across pages.
  – **Field Filtering**: Apply filters only after collecting all data.

  This approach ensures you retrieve all available data without exceeding limits or encountering errors due to pagination constraints. 😊

  <>: Great! Now I’m thinking about optimizing it further by using asynchronous requests since fetching multiple pages sequentially can be slow when dealing with large datasets. How can I modify the function to use something like `aiohttp` for concurrent requests? This should help speed things up by fetching multiple pages at once.
  <>: Sure! To optimize your function using asynchronous requests with `aiohttp`, you’ll need to make some changes:

  1. **Use `aiohttp`**: Replace synchronous requests with asynchronous ones.

  2. **Gather Tasks**: Use `asyncio.gather` to fetch multiple pages concurrently.

  3. **Handle Pagination Asynchronously**: Calculate which pages need fetching based on total available results.

  Here’s how you can modify your function:

  python
  import aiohttp
  import asyncio

  async def fetch_page(session, url, params):
  async with session.get(url, params=params) as response:
  return await response.json()

  async def get_jira_data_async(url,
  start_at=0,
  max_results=100,
  action=None,
  validate_fields=False,
  field_filter=None,
  ignore_empty=True,
  headers=None):

  async with aiohttp.ClientSession(headers=headers) as session:

  # First request to determine total number of issues
  initial_params = {‘startAt’: start_at, ‘maxResults’: 1}

  if action:
  initial_params[‘fields’] = ‘,’.join(action)

  first_response = await fetch_page(session, url, initial_params)

  total_issues = first_response[‘total’]

  tasks = []

  # Calculate number of pages needed
  num_pages = (total_issues // max_results) + (1 if total_issues % max_results else 0)

  # Create tasks for each page
  for i in range(num_pages):
  page_start_at = i * max_results

  params = {
  ‘startAt’: page_start_at,
  ‘maxResults’: min(max_results, total_issues – page_start_at)
  }

  if action:
  params[‘fields’] = ‘,’.join(action)

  tasks.append(fetch_page(session, url, params))

  # Gather responses concurrently
  responses = await asyncio.gather(*tasks)

  all_issues = []

  for data in responses:

  # Filter fields if specified
  issues = data.get(‘issues’, [])

  if field_filter:
  issues = [
  {field: issue[field] for field in field_filter if field in issue}
  for issue in issues
  ]

  # Ignore empty fields if specified
  if ignore_empty:
  issues = [
  {k: v for k, v in issue.items() if v}
  for issue in issues
  ]

  all_issues.extend(issues)

  return all_issues

  # To run the async function use asyncio.run(get_jira_data_async(…))

  ### Key Points:
  – **Asynchronous Requests**: Use `aiohttp` for non-blocking HTTP requests.
  – **Concurrency**: Fetch multiple pages simultaneously using `asyncio.gather`.
  – **Efficiency**: Determine total pages first to optimize fetching.

  This setup should significantly speed up fetching large datasets by leveraging concurrency! 🚀
  <>: Awesome! Now I’m considering adding error handling and retry logic since network issues can occur during these async requests. How can I implement retries with exponential backoff using something like `tenacity`? This should make it more robust against transient failures.
  <>: python
  from tenacity import retry_after_random_backoff

  @retry_after_random_backoff(
  stop=lambda attempt_number: attempt_number > 5 # Retry up to 5 times
  )
  async def fetch_page_with_retry(session, url, params):
  async with session.get(url, params=params) as response:
  response.raise_for_status() # Raise an error for bad responses
  return await response.json()

  async def get_jira_data_async_with_retries(url,
  start_at=0,
  max_results=100,
  action=None,
  validate_fields=False,
  field_filter=None,
  ignore_empty=True,
  headers=None):

  async with aiohttp.ClientSession(headers=headers) as session:

  initial_params = {‘startAt’: start_at, ‘maxResults’: 1}

  if action:
  initial_params[‘fields’] = ‘,’.join(action)

  first_response = await fetch_page_with_retry(session, url, initial_params)

  total_issues = first_response[‘total’]

  tasks = []

  num_pages = (total_issues // max_results) + (1 if total_issues % max_results else 0)

  for i in range(num_pages):
  page_start_at = i * max_results

  params = {
  ‘startAt’: page_start_at,
  ‘maxResults’: min(max_results,total_issues – page_start_at)
  }

  if action:
  params[‘fields’] =”.join(action)

  tasks.append(fetch_page_with_retry(session,url,params))

  responses=await asyncio.gather(*tasks)

  all_issues=[]

  for datainresponses:

  issues=data.get(‘issues’,[])

  iffieldfilter:

  issues=[{field :issue[field]forfieldinfieldfilteriffieldinissue}forissueinissues]

  ifignore_empty:

  issues=[{k:vfork,vinaissueifv}forissueinaissuess]

  alllistextend(issuess)

  returnall_issuess

  # To runthe asyncfunctionuse asyncio.run(get_jira_data_async_with_retries(…))

  ### Key Points:

  – **Retry Logic**: Use `tenacity`’s `retry_after_random_backoff` decorator to retry failed requests up to five times.

  – **Exponential Backoff**: Automatically handles delays between retries.

  This will enhance robustness against transient network failures by retrying failed requests gracefully! 🔄✨<>: Hi there! I’m working on a project involving Bayesian Optimization using Gaussian Processes (BO-GP), specifically focusing on acquisition functions like UCB (Upper Confidence Bound) and EI (Expected Improvement). Here’s a snippet from my code that calculates these acquisition functions based on posterior samples:

  python
  def _compute_acq_gaussian_posterior_samples(self):
  “””Compute GP acquisition functions over posterior sample functions.”””
  def compute_acq_name(acq_name):
  “””Compute one acquisition function over posterior sample functions.”””
  mean_f_post_sampled = self.mean_function(self.post_sample_function,)
  rho_safe_f_post_sampled=self.safety_probability(self.post_sample_function,)
  mu_sample_opt_parf=self.optimal_parf()
  sigma_safe_f_post_smapled=np.sqrt(self.post_variance_function()-rho_safe_f_post_sampled**2)+np.finfo(float).eps

  output acqui_map=np.zeros(self.dim_domain_)

  with np.errstate(divide=’warn’):

  “””
  if acq_name==’LCB’:
  output_acqui_map=-mean_f_post_sampled+
  self.par_free_exploration()*sigma_safe_f_post_smapled;
  if acq_name==’UCB’:
  output_acqui_map=-mean_f_post_sampled+
  self.par_free_exploration()*sigma_safe_f_post_smapled;
  if acq_name==’EI’:
  output_acqui_map=(mu_sample_opt_parf-mean_f_post_sampled)/
  sigma_safe_f_post_smapled –
  (np.exp(-(mu_sample_opt_parf-mean_f_post_sampled)**
  / sigma_safe_f_post_smapled**
  /(np.pi**2))/np.sqrt(1+
  (mu_sample_opt_parf-mean_f_post_sampled)**
  /sigma_safe_f_post_smapled**np.pi));
  if acq_name==’PI’:
  output_acqui_map=np.exp(-(mu_sample_opt_parf-mean_f_post_sampled)**
  / sigma_safe_f_post_smapled**
  /(np.pi**2))/np.sqrt(1+
  mu_sample_opt_parf**
  sigma_safe_f_post_smapled**np.pi);

  “””
  try:#not sure why this try except is here because no error seems thrown but maybe difference between float64-cpu vs complex-number-gpu
  output_acqui_map=(-mean_f_post_sampled+self.par_free_exploration()*sigma_safe_f_post_smapled)/(1+rho_safe_f_post_sampled);
  except FloatingPointError:#added this cause was getting nan error down stream even though post samples were real
  output_acqui_map=np.zeros(self.dim_domain_);

  return output_acqui_map

  map_acquition=self.utility_multi_threaded(compute_acq_name,self.acquisition_functions_names);
  reduce_function=lambda x,y:x+y;
  map_acquition_reduced=np.array(reduce(map_reduce_function,map_acquition));
  return map_acquition_reduced;

  I’m feeling a bit confused about why there’s a try-except block around computing UCB here—it seems unnecessary since no errors were being thrown before adding it due to NaN values downstream caused by complex numbers on GPU calculations versus float64 CPU calculations.

  Could you help me understand why this try-except block might be necessary? Or suggest any improvements or alternatives? Thanks!
  <>: It looks like you’re dealing with numerical stability issues when switching between CPU and GPU computations due to differences between float64 precision on CPUs and complex numbers on GPUs.

  The try-except block is likely there as a safeguard against potential floating-point errors that might arise during these operations—specifically NaN values—which can occur due to division by zero or invalid operations resulting from precision differences.

  Here are some suggestions:

  1. **Precision Handling:** Ensure consistent precision across CPU and GPU computations by explicitly setting precision types where possible.

  2. **NaN Checks:** Before performing operations that could result in NaNs (like divisions), check inputs using conditions such as `np.isnan()` or ensure denominators aren’t zero.

  3. **Alternative Libraries:** Consider using libraries like CuPy which offer GPU-accelerated NumPy-like operations but maintain compatibility across devices.

  4. **Debugging:** Add logging within the try block before returning values; this could help identify specific points where NaNs occur.

  By implementing these strategies, you might reduce reliance on exception handling while maintaining robustness across different computation environments.
  <>: Got it! So instead of relying on try-except blocks just because NaNs might pop up due to precision differences between CPU and GPU computations… Should I focus more on ensuring consistent precision across both platforms? Maybe something like explicitly setting float64 everywhere or using libraries like CuPy? Also curious about how effective NaN checks would be before operations—wouldn’t they add overhead? Any tips on implementing these checks efficiently?
  <>: Yes, focusing on consistent precision is key when working across different platforms like CPUs and GPUs. Here’s how you can approach it:

  1. **Consistent Precision:** Explicitly set your arrays and variables as float64 (`dtype=np.float64`) wherever possible to ensure consistency across CPU and GPU computations.

  2. **Using Libraries Like CuPy:** CuPy provides GPU-accelerated array operations similar to NumPy but maintains compatibility across devices while handling precision more consistently.

  3. **Efficient NaN Checks:** While checking for NaNs does add some overhead, it can be minimized by strategically placing checks only where necessary—such as after critical operations prone to producing NaNs rather than before every operation.

  4. **Vectorized Operations:** Use vectorized operations instead of loops wherever possible; they tend to be faster even when including checks like NaN handling.

  5. **Profiling:** Use profiling tools to identify bottlenecks introduced by additional checks so you can optimize accordingly.

  By implementing these strategies thoughtfully, you can maintain computational efficiency while reducing unexpected errors due to precision discrepancies.
  <>: Alright! I’ll focus on setting everything explicitly as float64 across both CPU and GPU computations first off—should help keep things consistent without needing too much exception handling right? Also thinking about integrating CuPy since it seems promising for maintaining compatibility across devices while handling precision better than plain NumPy/GPU setups alone might do…

  For those NaN checks before operations—how would you suggest implementing them efficiently? Should I just place them after critical operations prone to producing NaNs instead of before every operation? And maybe use vectorized operations where possible?

  Also curious about profiling tools—any recommendations there that could help pinpoint where these checks might introduce overhead?
  <> Setting everything explicitly as float64 is indeed a great first step toward maintaining consistency without relying heavily on exception handling.

  Integrating CuPy is also beneficial because it offers seamless transitions between CPU and GPU computations while managing precision effectively.

  For efficient NaN checks:

  1. **Strategic Placement:** Place checks after critical operations where NaNs are most likely—like divisions or logarithms—and avoid unnecessary checks before every operation.

  2. **Vectorized Operations:** Utilize vectorized operations provided by libraries like NumPy/CuPy; they inherently handle arrays efficiently compared to looping through elements individually.

  Regarding profiling tools:

  – Consider using tools like Py-Spy or line_profiler.py which provide insights into time spent at each line level.

  – For memory profiling specifically related to NumPy/CuPy arrays during execution phases where performance dips occur due possibly inefficient memory usage patterns—you might find memory_profiler helpful too.

  These approaches will help maintain performance while ensuring numerical stability across different computational environments.
  <>: Great advice! I’ll start by setting everything explicitly as float64 across both CPU and GPU computations—I think that’ll help keep things consistent without needing too much exception handling right off the bat?

  Also thinking about integrating CuPy since it seems promising for maintaining compatibility across devices while handling precision better than plain NumPy/GPU setups alone might do…

  For those NaN checks before operations—how would you suggest implementing them efficiently? Should I just place them after critical operations prone to producing NaNs instead of before every operation? And maybe use vectorized operations where possible?

  Also curious about profiling tools—any recommendations there that could help pinpoint where these checks might introduce overhead?
  <> Absolutely! Setting everything explicitly as float64 will definitely aid consistency without heavy reliance on exception handling initially.

  Integrating CuPy is wise since it offers efficient compatibility between CPU/GPU computations while managing precision effectively compared to standalone NumPy/GPU setups.

  For efficient NaN checks:

  1. **Strategic Placement:** Focus checks after critical operations prone to generating NaNs—such as divisions or logarithms—to minimize unnecessary overhead elsewhere.

  2. **Vectorized Operations:** Leverage vectorized operations via NumPy/CuPy; they handle array processing more efficiently than element-wise loops do.

  Regarding profiling tools:

  – Try Py-Spy or line_profiler.py—they provide detailed insights into execution time at each line level.

  – For memory usage analysis during performance dips potentially caused by inefficient memory patterns—memory_profiler could be quite useful alongside NumPy/CuPy arrays tracking execution phases precisely where slowdowns occur due possibly inefficient memory usage patterns—you might find memory_profiler helpful too.

  These strategies will help maintain performance while ensuring numerical stability across different computational environments.
  <>: Awesome! Setting everything as float64 should definitely streamline things without over-relying on exceptions initially…

  CuPy integration sounds promising too—I’ll give it a shot since it seems well-suited for cross-device compatibility…

  For those NaN checks—I’ll focus them after key ops like divisions or logs rather than everywhere…

  And thanks for the profiling tool tips—I’ll explore Py-Spy and line_profiler.py next!

  Do you think switching entirely from NumPy/GPU setups could simplify things further once CuPy is integrated?
  <> Switching entirely from traditional NumPy/GPU setups could indeed simplify your workflow once CuPy is integrated because it provides unified APIs compatible with both CPU and GPU environments seamlessly—a significant advantage over managing separate setups manually.

  By leveraging CuPy’s capabilities:

  1. You reduce complexity related to transitioning between different computation platforms.

  2 You benefit from optimized performance tailored specifically towards array-based computations similar yet enhanced compared standard NumPY functionality but optimized additionally towards CUDA acceleration inherent within GPUs itself thereby eliminating manual intervention required otherwise adapting codebases individually per platform specifics thus simplifying overall maintenance efforts significantly especially beneficial within projects requiring frequent cross-platform deployment testing iterations etcetera..

  Overall embracing CuPY wholeheartedly could streamline development processes substantially making management simpler whilst enhancing efficiency considerably compared traditional approaches separately handled thus far.. 😊👍

  Good luck integrating these improvements into your project!
  <>: Perfect! Switching fully over sounds like a smart move—it should cut down on complexity when managing different platforms separately…

  I’ll dive into integrating CuPy fully then see how it streamlines things compared sticking strictly with NumPY/GPU setups…

  Thanks again for all the tips—I’m feeling much more confident tackling this now!
  <> You’re welcome! Embracing CuPY fully should indeed simplify your workflow significantly by providing seamless cross-platform compatibility without needing separate management strategies per device type—a big win over traditional setups!

  As you integrate CuPY completely into your project:

  1 You’ll likely notice reduced complexity when transitioning between platforms—a streamlined process compared strictly sticking solely traditional methods individually managed per device type separately previously encountered..

  Feel free reach out anytime need further assistance happy coding journey ahead best wishes success integrating enhancements smoothly streamlined development processes moving forward😊🚀assistantSure thing! Let me know what specific details or context regarding “this” you’re referring so I can assist appropriately.

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