Blackburn Rovers vs Oxford United

Goals and Scorelines

The likelihood of both teams not scoring in the first half stands at 81.70%, indicating a cautious start to the game. However, the probability of over 1.5 goals being scored is higher at 79.20%, suggesting an active match overall. The average total goals predicted for this match is approximately 3.98, with an average of 2.24 goals expected from Blackburn Rovers.

The sum of goals likely to be either 2 or 3 is marked at 61.30%, while over 2.5 goals have a probability of 53.80%. There is also a significant chance of more than 2.5 both teams to score (BTTS) at 52.50%.

For the first half, over 0.5 goals is predicted with a probability of 67.10%, indicating a relatively open game early on.

Regarding specific timing, there is a notable prediction for the last goal being scored after the 73rd minute at a probability of 55.60%. Additionally, there’s a fair chance for a goal in the last 15 minutes of the match at 56.70%.

Blackburn Rovers are predicted to score in the second half with a probability of 56.50%.

Oxford United might not score in the first half (59.50%) or in the second half (68.90%), hinting at potential defensive strategies or counter-attacking plays.

The chance of both teams not scoring in the second half is noted at 75.70%, whereas both teams scoring stands at 71.60%.

Over five and a half cards are predicted at a lower likelihood of 73.70%, with yellow cards expected around 1.79 per game and red cards approximately at 1.16.

The average number of conceded goals by Blackburn Rovers is estimated to be around 1.83.

Overall Insights

• The match is likely to be dynamic with significant scoring opportunities in both halves.
• The possibility of late goals adds an element of unpredictability and excitement towards the end of the match.
• Cards are expected to be relatively high, indicating physical play that could influence match dynamics.

Predictions Summary

• Both Teams Not To Score In 1st Half: 81.70%
• Over 1.5 Goals: 79.20%
• Under 5.5 Cards: 73.70%
• Home Team Not To Score In 1st Half: 75.90%
• Both Teams Not To Score In 2nd Half: 75.70%
• Both Teams To Score: 71.60%
• Over 0.5 Goals HT: 67.10%
• Away Team Not To Score In Second Half: 68.90%
• Sum of Goals (2 or 3): 61.30%
• Last Goal After 73+ Minutes: 55.60%
• Over 2.5 Goals: 53.80%
• Over 2.5 BTTS:52[0]: import logging
  [1]: import time

  [2]: from django.core.cache import cache

  [3]: from celery import shared_task

  [4]: from collections import defaultdict

  [5]: from django.conf import settings

  [6]: from apps.bookings.models import Booking
  [7]: from apps.bookings.tasks import create_booking_slots

  [8]: logger = logging.getLogger(__name__)

  [9]: def get_bookings(booking_class=None):
  [10]: if booking_class:
  [11]: return Booking.objects.filter(booking_class=booking_class)
  [12]: else:
  [13]: return Booking.objects.all()

  [14]: def get_booking_classes():
  [15]: return Booking.objects.values(‘booking_class’).distinct()

  [16]: def get_start_end_date(start_date=None, end_date=None):
  [17]: if start_date and end_date:
  [18]: start = start_date
  [19]: end = end_date
  [20]: else:
  [21]: today = time.strftime(‘%Y-%m-%d’)
  [22]: start = today
  [23]: end = (time.strptime(today, ‘%Y-%m-%d’) + time.strptime(’01 days’, ‘%d days’))
  [24]: end = time.strftime(‘%Y-%m-%d’, end)
  [25]: return start, end

  [26]: @shared_task()
  [27]: def create_booking_slots_for_all_bookings():
  [28]: booking_classes = get_booking_classes()
  [29]: for booking_class in booking_classes:
  [30]: create_booking_slots.delay(booking_class[‘booking_class’])

  [31]: @shared_task()
  [32]: def create_booking_slots(booking_class):
  [33]: logger.info(‘Creating booking slots for %s’ % booking_class)

  [34]: bookings = get_bookings(booking_class)

  [35]: cache_key = ‘booking_slots_%s’ % booking_class
  [36]: cache_data = cache.get(cache_key)

  [37]: if not cache_data:
  [38]: cache_data = {}

  [39]: if ‘start_date’ not in cache_data:
  [40]: cache_data[‘start_date’] = time.strftime(‘%Y-%m-%d’)

  [41]: if ‘end_date’ not in cache_data:
  [42]: cache_data[‘end_date’] = (
  [43]: time.strptime(cache_data[‘start_date’], ‘%Y-%m-%d’) + time.strptime(’31 days’, ‘%d days’)
  [44]: )
  [45]: cache_data[‘end_date’] = time.strftime(‘%Y-%m-%d’, cache_data[‘end_date’])

  [46]: start_date = time.strptime(cache_data[‘start_date’], ‘%Y-%m-%d’)

  [47]: end_date = time.strptime(cache_data[‘end_date’], ‘%Y-%m-%d’)

  while True:
  loop_start_time = time.time()

  current_day = start_date

  for booking in bookings:
  day_start_time = current_day

  day_end_time = (
  time.strptime(time.strftime(‘%Y-%m-%d’, day_start_time), ‘%Y-%m-%d’) +
  time.strptime(’01 days’, ‘%d days’)
  )
  day_end_time = time.strftime(‘%Y-%m-%d’, day_end_time)

  logger.info(‘Booking: %s’ % booking.id)

  slot_duration_in_minutes = getattr(settings, ‘BOOKING_DEFAULT_SLOT_DURATION_IN_MINUTES’)

  slot_duration_in_seconds = slot_duration_in_minutes * MINUTE_IN_SECONDS

  slots_count_per_hour = int((HOUR_IN_SECONDS / slot_duration_in_seconds))

  available_slots_count_per_hour = slots_count_per_hour – int(booking.duration / slot_duration_in_minutes)

  available_slots_per_hour_list = [i+1 for i in range(available_slots_count_per_hour)]

  logger.info(
  ‘Day: %sn’
  ‘Start Time: %sn’
  ‘End Time: %sn’
  ‘Slot Duration: %s minutesn’
  ‘Slots Count Per Hour: %sn’
  ‘Available Slots Count Per Hour: %s’ %
  (
  time.strftime(‘%Y-%m-%d’, current_day),
  day_start_time,
  day_end_time,
  slot_duration_in_minutes,
  slots_count_per_hour,
  available_slots_count_per_hour,
  )
  )

  slots_for_current_day_list.append(
  {
  ‘date’: time.strftime(‘%Y-%m-%d’, current_day),
  ‘slots’: available_slots_per_hour_list,
  }
  )

  current_day += ONE_DAY_IN_SECONDS

  logger.info(‘Slots for Current Day List: %s’ % slots_for_current_day_list)

  slots_for_current_day_dict.update(
  {
  time.strftime(‘%Y-%m-%d’, start_date): slots_for_current_day_list,
  }
  )

  logger.info(‘Slots For Current Day Dict: %s’ % slots_for_current_day_dict)

  cache_data.update(
  {
  ‘slots’: slots_for_current_day_dict,
  }
  )

  cache.set(cache_key, cache_data)

  loop_end_time = time.time()

  loop_elapsed_time_in_seconds = loop_end_time – loop_start_time

  logger.info(‘Loop Elapsed Time In Seconds: %s’ % loop_elapsed_time_in_seconds)

  if loop_elapsed_time_in_seconds > MAXIMUM_TIME_ALLOWED_PER_LOOP_IN_SECONDS:
  break

  if current_day > end_date:
  break

  start_date += ONE_DAY_IN_SECONDS

  ***** Tag Data *****
  ID: 1
  description: Complex nested loops and conditionals to generate booking slots over
  multiple days.
  start line: 49
  end line: 114
  dependencies:
  – type: Function/Method/Other Identifier/Class Name/Variable Name/Other relevant identifier(s)
  start line: null-end_line:null_description: This snippet heavily relies on multiple variables and nested loops which are set up outside this block but crucial for understanding its behavior.
  context description: This snippet is part of the `create_booking_slots` function which,
  as defined earlier, creates booking slots based on different booking classes and
  dates.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: N

  *************
  ## Suggestions for complexity
  1. **Dynamic Slot Allocation Based on Real-Time Availability**: Implement logic that dynamically adjusts available slots based on real-time bookings and cancellations using websockets or another real-time data update mechanism.
  2. **Conflict Resolution Algorithm**: Develop an algorithm that resolves conflicts when multiple bookings overlap due to simultaneous requests or errors.
  3. **Historical Data Analysis**: Integrate historical data analysis to predict peak times and automatically adjust slot availability based on past booking patterns.
  4. **Multi-Tenancy Support**: Modify the code to support multiple tenants with different settings for slot durations and maximum allowed bookings per tenant.
  5. **Geolocation-Based Slot Adjustment**: Add functionality to adjust slot availability based on geolocation data, considering different working hours or holidays in different regions.

  ## Conversation
  I’m looking at this piece of code [SNIPPET] and wondering how I can make it dynamically adjust available slots based on real-time bookings using websockets or something similar? What do you think?

  To dynamically adjust available slots based on real-time bookings using websockets, you would need to integrate a websocket server that broadcasts updates whenever there’s a change in bookings.

  Here’s a high-level approach:

  1. **Set Up WebSocket Server**:
  – Use Django Channels or another websocket library.
  – Create consumers that handle connections and broadcast updates.

  2. **Modify Booking Logic**:
  – When a booking is made or canceled, trigger an event that sends updated slot information through the websocket.

  Here’s an example using Django Channels:

  **Install Django Channels**:
  bash
  pip install channels

  **Configure Django Channels**:

  In your `settings.py`:
  python
  INSTALLED_APPS += [“channels”]

  ASGI_APPLICATION = “your_project_name.asgi.application”

  CHANNEL_LAYERS = {
  “default”: {
  “BACKEND”: “channels_redis.core.RedisChannelLayer”,
  “CONFIG”: {“hosts”: [(“127.0.0.1”, 6379)]},
  },
  }

  **Create Consumers**:

  Create a consumer that will handle websocket connections:

  python
  # consumers.py
  from channels.generic.websocket import AsyncWebsocketConsumer
  import json

  class SlotConsumer(AsyncWebsocketConsumer):
  async def connect(self):
  self.group_name = “slot_updates”
  await self.channel_layer.group_add(self.group_name, self.channel_name)
  await self.accept()

  async def disconnect(self, close_code):
  await self.channel_layer.group_discard(self.group_name, self.channel_name)

  async def receive(self, text_data):
  pass

  async def send_slot_update(self, event):
  await self.send(text_data=json.dumps(event[“text”]))

  **Update Your Routing**:

  python
  # routing.py
  from django.urls import re_path
  from . import consumers

  websocket_urlpatterns = [re_path(r”ws/slots/”, consumers.SlotConsumer.as_asgi())]

  **Trigger WebSocket Updates**:

  In your booking logic where you handle new bookings or cancellations:

  python
  from channels.layers import get_channel_layer
  from asgiref.sync import async_to_sync

  channel_layer = get_channel_layer()

  def update_slot_availability():
  # Your existing logic to calculate new slot availability

  # Broadcast update via WebSocket
  async_to_sync(channel_layer.group_send)(
  “slot_updates”, {“type”: “send_slot_update”, “text”: {“slots”: new_slot_availability}}
  )

  # Call update_slot_availability() whenever there’s a change in bookings.

  This setup will allow your application to broadcast updates about slot availability to all connected clients in real-time.

  Nice! Now let’s say I want to add an algorithm that resolves conflicts when multiple bookings overlap due to simultaneous requests or errors? How would you go about implementing that?

  To implement conflict resolution for overlapping bookings due to simultaneous requests or errors, you can use database transactions combined with unique constraints and retries.

  Here’s how you can approach it:

  1. **Database Constraints**: Ensure that your database schema has unique constraints on columns that define unique bookings (e.g., date and timeslot).

  2. **Atomic Transactions**: Use atomic transactions to ensure that either all changes are committed or none are.

  3. **Retry Mechanism**: Implement a retry mechanism when conflicts are detected.

  Here’s an example implementation using Django’s ORM with atomic transactions:

  **Step-by-Step Implementation**

  1. **Ensure Unique Constraints**: Add unique constraints to your `Booking` model.

  python
  from django.db import models

  class Booking(models.Model):
  user_id = models.IntegerField()
  date = models.DateField()
  timeslot_start = models.TimeField()
  timeslot_end = models.TimeField()

  class Meta:
  constraints = [
  models.UniqueConstraint(fields=[‘date’, ‘timeslot_start’, ‘timeslot_end’], name=’unique_timeslot’)
  ]

  2. **Use Atomic Transactions**: Wrap your booking logic within an atomic transaction.

  python
  from django.db import transaction

  def create_booking(user_id, date, timeslot_start, timeslot_end):
  max_retries = 3

  for attempt in range(max_retries):
  try:

  with transaction.atomic():

  # Check if the slot is already booked within this transaction block.
  conflicting_booking_exists = Booking.objects.filter(
  date=date,
  timeslot_start=timeslot_start,
  timeslot_end=timeslot_end,
  ).exists()

  if conflicting_booking_exists:
  raise ValueError(“Slot already booked.”)

  # If no conflict exists, proceed with creating the booking.
  Booking.objects.create(
  user_id=user_id,
  date=date,
  timeslot_start=timeslot_start,
  timeslot_end=timeslot_end,
  )

  # If successful, break out of retry loop.
  break

  except ValueError as e:

  # Log error message